VAS toggle based on device orientation

ABSTRACT

As noted above, example techniques relate to toggling a cloud-based VAS between enabled and disabled modes. An example implementation involves a NMD detecting that the housing is in a first orientation and enabling a first mode. Enabling the first mode includes disabling voice input processing via a cloud-based VAS and enabling local voice input processing. In the first mode, the NMD captures sound data associated with a first voice input and detects, via a local natural language unit, that the first voice input comprises sound data matching one or more keywords. The NMD determines an intent of the first voice input and performs a first command according to the determined intent. The NMD may detect that the housing is in a second orientation and enables the second mode. Enabling the second mode includes enabling voice input processing via the cloud-based VAS.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 U.S.C. § 120 to, and is acontinuation of, U.S. non-provisional patent application Ser. No.16/660,197, filed on Oct. 22, 2019, entitled “VAS Toggle Based On DeviceOrientation,” which is incorporated herein by reference in its entirety.

FIELD OF THE DISCLOSURE

The present technology relates to consumer goods and, more particularly,to methods, systems, products, features, services, and other elementsdirected to voice-assisted control of media playback systems or someaspect thereof.

BACKGROUND

Options for accessing and listening to digital audio in an out-loudsetting were limited until in 2002, when SONOS, Inc. began developmentof a new type of playback system. Sonos then filed one of its firstpatent applications in 2003, entitled “Method for Synchronizing AudioPlayback between Multiple Networked Devices,” and began offering itsfirst media playback systems for sale in 2005. The Sonos Wireless HomeSound System enables people to experience music from many sources viaone or more networked playback devices. Through a software controlapplication installed on a controller (e.g., smartphone, tablet,computer, voice input device), one can play what she wants in any roomhaving a networked playback device. Media content (e.g., songs,podcasts, video sound) can be streamed to playback devices such thateach room with a playback device can play back corresponding differentmedia content. In addition, rooms can be grouped together forsynchronous playback of the same media content, and/or the same mediacontent can be heard in all rooms synchronously.

BRIEF DESCRIPTION OF THE DRAWINGS

Features, aspects, and advantages of the presently disclosed technologymay be better understood with regard to the following description,appended claims, and accompanying drawings where:

Features, aspects, and advantages of the presently disclosed technologymay be better understood with regard to the following description,appended claims, and accompanying drawings, as listed below. A personskilled in the relevant art will understand that the features shown inthe drawings are for purposes of illustrations, and variations,including different and/or additional features and arrangements thereof,are possible.

FIG. 1A is a partial cutaway view of an environment having a mediaplayback system configured in accordance with aspects of the disclosedtechnology.

FIG. 1B is a schematic diagram of the media playback system of FIG. 1Aand one or more networks.

FIG. 2A is a functional block diagram of an example playback device.

FIG. 2B is an isometric diagram of an example housing of the playbackdevice of FIG. 2A.

FIG. 2C is a diagram of an example voice input.

FIG. 2D is a graph depicting an example sound specimen in accordancewith aspects of the disclosure.

FIGS. 3A, 3B, 3C, 3D and 3E are diagrams showing example playback deviceconfigurations in accordance with aspects of the disclosure.

FIG. 4 is a functional block diagram of an example controller device inaccordance with aspects of the disclosure.

FIGS. 5A and 5B are controller interfaces in accordance with aspects ofthe disclosure.

FIG. 6 is a message flow diagram of a media playback system.

FIG. 7A is a functional block diagram of an example network microphonedevice.

FIG. 7B is an isometric diagram of the example network microphone devicein a first orientation.

FIG. 7C is an isometric diagram of the example network microphone devicein a second orientation.

FIG. 7D is an isometric diagram illustrating the example networkmicrophone device transitioning from the first orientation to the secondorientation.

FIG. 7E is a functional block diagram of certain components of theexample network microphone device in accordance with aspects of thedisclosure.

FIG. 8A is a schematic diagram illustrating the example networkmicrophone device operating in a first mode while paired with an examplenetwork device.

FIG. 8B is a schematic diagram illustrating the example networkmicrophone device operating in a second mode while paired with theexample network device.

FIG. 9 is a schematic diagram illustrating an example media playbacksystem and cloud network in accordance with aspects of the disclosure.

FIGS. 10A, 10B, 10C, and 10D show exemplary output of an example NMDconfigured in accordance with aspects of the disclosure.

FIG. 11 is a flow diagram of an example method to toggle a VAS based onorientation in accordance with aspects of the disclosure.

The drawings are for purposes of illustrating example embodiments, butit should be understood that the inventions are not limited to thearrangements and instrumentality shown in the drawings. In the drawings,identical reference numbers identify at least generally similarelements. To facilitate the discussion of any particular element, themost significant digit or digits of any reference number refers to theFigure in which that element is first introduced. For example, element103 a is first introduced and discussed with reference to FIG. 1A.

DETAILED DESCRIPTION I. Overview

Example techniques described herein involve toggling voice inputprocessing via a cloud-based voice assistant service (“VAS”). An examplenetwork microphone device (“NMD”) may enable or disable processing ofvoice inputs via a cloud-based voice assistant service based on thephysical orientation of the NMD. While processing of voice inputs viathe cloud-based VAS is disabled, the NMD may process voice inputs via alocal natural language unit (NLU).

An NMD is a networked computing device that typically includes anarrangement of microphones, such as a microphone array, that isconfigured to detect sound present in the NMD's environment. NMDs mayfacilitate voice control of smart home devices, such as wireless audioplayback devices, illumination devices, appliances, and home-automationdevices (e.g., thermostats, door locks, etc.). NMDs may also be used toquery a cloud-based VAS for information such as search queries, news,weather, and the like.

Some users are apprehensive of sending their voice data to a cloud-basedVAS for privacy reasons. One possible advantage of a processing voiceinputs via a local NLU is increased privacy. By processing voiceutterances locally, a user may avoid transmitting voice recordings tothe cloud (e.g., to servers of a voice assistant service). Further, insome implementations, the NMD may use a local area network to discoverplayback devices and/or smart devices connected to the network, whichmay avoid providing personal data relating to a user's home to thecloud. Also, the user's preferences and customizations may remain localto the NMD(s) in the household, perhaps only using the cloud as anoptional backup. Other advantages are possible as well.

On the other hand, a cloud-based VAS is relatively more capable than alocal NLU. In contrast to a NLU implemented in one or more cloud serversthat is capable of recognizing a wide variety of voice inputs,practically, local NLUs are capable of recognizing a relatively smallerlibrary of keywords (e.g., 10,000 words and phrases). Moreover, thecloud-based VAS may support additional features (such a querying forreal-time information) relative to a local NLU. Moreover, thecloud-based VAS may integrate with other cloud-based services to providevoice control of those services.

Given these competing interests, a user may desire to selectivelydisable voice input processing via a cloud-based VAS (in favor of voiceinput processing via a local NLU). When voice input processing via thecloud-based VAS is disabled, the user has the benefit of increasedprivacy. Conversely, when voice input processing via the cloud-based VASis enabled, the user may take advantage of the relatively more capablecloud-based VAS.

Example NMDs may selectively disable voice input processing via acloud-based VAS using physical orientation of its housing. For instance,an example NMD may be implemented with a cylindrical-shaped housing(e.g., similar in shape to a hockey puck). The first and second ends ofthe housing may carry a first set of microphones and a second set ofmicrophones, respectively.

When the cylindrical-shaped housing is placed on its first end (i.e., ina first orientation), the NMD disables voice input processing via acloud-based VAS. Conversely, when the cylindrical-shaped housing isplaced on its second end (i.e., in a second orientation), the NMDenables voice input processing via a cloud-based VAS. Disablingcloud-based processing using physical orientation may instill confidencein a user that their privacy is being protected, as the microphonesassociated with the cloud-based VAS are partially covered by whateversurface the housing of the network microphone device is resting upon.

Other example network microphone devices may implement differenttoggling techniques. For instance, the NMD may include a physical switchor other hardware control to toggle voice processing via the cloud-basedVAS. Alternatively, a graphical user interface (GUI) or voice userinterface (VUI) may be used to toggle voice processing via thecloud-based VAS.

Example NMDs that selectively disable voice input processing via acloud-based VAS using physical orientation may include two or more setsof microphones. For instance, a first set of microphones may be utilizedto capture audio in a first orientation while a second set ofmicrophones is utilized in a second orientation. Continuing thepuck-shaped housing example above, the housing may carry one or morefirst microphones near its first end and one or more second microphonesin its second end. Then, when the housing is sitting on its first end,the NMD captures voice inputs using the one or more second microphones.Conversely, when the housing is sitting on its second end, the NMDcaptures voice inputs using the one or more first microphones.

Some cloud-based voice assistant services are triggered based on a wakeword. In such examples, a voice input typically includes a wake wordfollowed by an utterance comprising a user request. In practice, a wakeword is typically a predetermined nonce word or phrase used to “wake up”an NMD and cause it to invoke a particular voice assistant service(“VAS”) to interpret the intent of voice input in detected sound. Forexample, a user might speak the wake word “Alexa” to invoke the AMAZON®VAS, “Ok, Google” to invoke the GOOGLE® VAS, or “Hey, Siri” to invokethe APPLE® VAS, among other examples. In practice, a wake word may alsobe referred to as, for example, an activation-, trigger-, wakeup-word or-phrase, and may take the form of any suitable word, combination ofwords (e.g., a particular phrase), and/or some other audio cue.

To identify whether sound detected by the NMD contains a voice inputthat includes a particular wake word, NMDs often utilize a wake-wordengine, which is typically onboard the NMD. The wake-word engine may beconfigured to identify (i.e., “spot” or “detect”) a particular wake wordin recorded audio using one or more identification algorithms. Suchidentification algorithms may include pattern recognition trained todetect the frequency and/or time domain patterns that speaking the wakeword creates. This wake-word identification process is commonly referredto as “keyword spotting.” In practice, to help facilitate keywordspotting, the NMD may buffer sound detected by a microphone of the NMDand then use the wake-word engine to process that buffered sound todetermine whether a wake word is present in the recorded audio.

When a wake-word engine detects a wake word in recorded audio, the NMDmay determine that a wake-word event (i.e., a “wake-word trigger”) hasoccurred, which indicates that the NMD has detected sound that includesa potential voice input. The occurrence of the wake-word event typicallycauses the NMD to perform additional processes involving the detectedsound. With a VAS wake-word engine, these additional processes mayinclude extracting detected-sound data from a buffer, among otherpossible additional processes, such as outputting an alert (e.g., anaudible chime and/or a light indicator) indicating that a wake word hasbeen identified. Extracting the detected sound may include reading outand packaging a stream of the detected-sound according to a particularformat and transmitting the packaged sound-data to an appropriate VASfor interpretation.

In turn, the VAS corresponding to the wake word that was identified bythe wake-word engine receives the transmitted sound data from the NMDover a communication network. A VAS traditionally takes the form of aremote service implemented using one or more cloud servers configured toprocess voice inputs (e.g., AMAZON's ALEXA, APPLE's SIRI, MICROSOFT'sCORTANA, GOOGLE'S ASSISTANT, etc.). In some instances, certaincomponents and functionality of the VAS may be distributed across localand remote devices.

When a VAS receives detected-sound data, the VAS processes this data,which involves identifying the voice input and determining intent ofwords captured in the voice input. The VAS may then provide a responseback to the NMD with some instruction according to the determinedintent. Based on that instruction, the NMD may cause one or more smartdevices to perform an action. For example, in accordance with aninstruction from a VAS, an NMD may cause a playback device to play aparticular song or an illumination device to turn on/off, among otherexamples. In some cases, an NMD, or a media system with NMDs (e.g., amedia playback system with NMD-equipped playback devices) may beconfigured to interact with multiple VASes. In practice, the NMD mayselect one VAS over another based on the particular wake word identifiedin the sound detected by the NMD.

Within examples, local processing of a voice input may be trigged basedon detection of one or more keywords in sound captured by the NMD.Example NMDs may include a local voice input engine to detect “localkeywords” and generate events to process voice inputs when a localkeyword is detected. These local keywords may take the form of a noncekeyword (e.g., “Hey, Sonos”) or a keyword that invokes a command(referred to herein as a “command keyword”). A command keyword is a wordor phrase that functions as a command itself, rather than being a nonceword that merely triggers a wake word event.

As noted above, a detected local keyword event may cause one or moresubsequent actions, such as local natural language processing of a voiceinput. For instance, when a local voice input engine detects a localkeyword in recorded audio, the NMD may determine that a local keywordevent has occurred and responsively process the voice input locallyusing a local NLU. Processing the input may involve the local NLUdetermining an intent from one or more keywords in the voice input.

In some implementations, voice input processing via the local NLU mayremain enabled when the voice input processing via the cloud-based VASis enabled. In such embodiments, a user may target the cloud-based VASfor processing a voice input by speaking a VAS wake word. The user maytarget the local NLU for processing of the voice input by speaking alocal wake word or by speaking a voice command without a VAS wake word.Alternatively, the NMD may disable voice input processing via the localNLU when voice input processing via the cloud-based VAS is enabled.

As noted above, example techniques relate to toggling a cloud-based VASbetween enabled and disabled modes. An example implementation involves anetwork microphone device including one or more first microphones, oneor more second microphones, a network interface, one or more processors,and a housing carrying the one or more first microphones, the one ormore second microphones, the network interface, the one or moreprocessors, and data storage having stored therein instructionsexecutable by the one or more processors. The network microphone devicedetects that the housing is in a first orientation. After detecting thatthe housing is in the first orientation, the device enables a firstmode. Enabling the first mode includes (i) disabling voice inputprocessing via a cloud-based voice assistant service and (ii) enablingvoice input processing via a local natural language unit. While thefirst mode is enabled, the network microphone device (i) captures sounddata associated with a first voice input via the one or more firstmicrophones and (ii) detects, via a local natural language unit, thatthe first voice input comprises sound data matching one or more keywordsfrom a local natural language unit library of the local natural languageunit. The network microphone device determines, via the local naturallanguage unit, an intent of the first voice input based on at least oneof the one or more keywords and performs a first command according tothe determined intent of the first voice input. The network microphonedevice may detects that the housing is in a second orientation differentthan the first orientation. After detecting that the housing is in thesecond orientation, the network microphone device enables the secondmode. Enabling the second mode includes enabling voice input processingvia the cloud-based voice assistant service.

While some embodiments described herein may refer to functions performedby given actors, such as “users” and/or other entities, it should beunderstood that this description is for purposes of explanation only.The claims should not be interpreted to require action by any suchexample actor unless explicitly required by the language of the claimsthemselves.

Moreover, some functions are described herein as being performed “basedon” or “in response to” another element or function. “Based on” shouldbe understood that one element or function is related to anotherfunction or element. “In response to” should be understood that oneelement or function is a necessary result of another function orelement. For the sake of brevity, functions are generally described asbeing based on another function when a functional link exists; however,such disclosure should be understood as disclosing either type offunctional relationship.

II. Example Operation Environment

FIGS. 1A and 1B illustrate an example configuration of a media playbacksystem 100 (or “MPS 100”) in which one or more embodiments disclosedherein may be implemented. Referring first to FIG. 1A, the MPS 100 asshown is associated with an example home environment having a pluralityof rooms and spaces, which may be collectively referred to as a “homeenvironment,” “smart home,” or “environment 101.” The environment 101comprises a household having several rooms, spaces, and/or playbackzones, including a master bathroom 101 a, a master bedroom 101 b,(referred to herein as “Nick's Room”), a second bedroom 101 c, a familyroom or den 101 d, an office 101 e, a living room 101 f, a dining room101 g, a kitchen 101 h, and an outdoor patio 101 i. While certainembodiments and examples are described below in the context of a homeenvironment, the technologies described herein may be implemented inother types of environments. In some embodiments, for example, the MPS100 can be implemented in one or more commercial settings (e.g., arestaurant, mall, airport, hotel, a retail or other store), one or morevehicles (e.g., a sports utility vehicle, bus, car, a ship, a boat, anairplane), multiple environments (e.g., a combination of home andvehicle environments), and/or another suitable environment wheremulti-zone audio may be desirable.

Within these rooms and spaces, the MPS 100 includes one or morecomputing devices. Referring to FIGS. 1A and 1B together, such computingdevices can include playback devices 102 (identified individually asplayback devices 102 a-102 o), network microphone devices 103(identified individually as “NMDs” 103 a-102 i), and controller devices104 a and 104 b (collectively “controller devices 104”). Referring toFIG. 1B, the home environment may include additional and/or othercomputing devices, including local network devices, such as one or moresmart illumination devices 108 (FIG. 1B), a smart thermostat 110, and alocal computing device 105 (FIG. 1A). In embodiments described below,one or more of the various playback devices 102 may be configured asportable playback devices, while others may be configured as stationaryplayback devices. For example, the headphones 102 o (FIG. 1B) are aportable playback device, while the playback device 102 d on thebookcase may be a stationary device. As another example, the playbackdevice 102 c on the Patio may be a battery-powered device, which mayallow it to be transported to various areas within the environment 101,and outside of the environment 101, when it is not plugged in to a walloutlet or the like.

With reference still to FIG. 1B, the various playback, networkmicrophone, and controller devices 102, 103, and 104 and/or othernetwork devices of the MPS 100 may be coupled to one another viapoint-to-point connections and/or over other connections, which may bewired and/or wireless, via a network 111, such as a LAN including anetwork router 109. For example, the playback device 102 j in the Den101 d (FIG. 1A), which may be designated as the “Left” device, may havea point-to-point connection with the playback device 102 a, which isalso in the Den 101 d and may be designated as the “Right” device. In arelated embodiment, the Left playback device 102 j may communicate withother network devices, such as the playback device 102 b, which may bedesignated as the “Front” device, via a point-to-point connection and/orother connections via the NETWORK 111.

As further shown in FIG. 1B, the MPS 100 may be coupled to one or moreremote computing devices 106 via a wide area network (“WAN”) 107. Insome embodiments, each remote computing device 106 may take the form ofone or more cloud servers. The remote computing devices 106 may beconfigured to interact with computing devices in the environment 101 invarious ways. For example, the remote computing devices 106 may beconfigured to facilitate streaming and/or controlling playback of mediacontent, such as audio, in the home environment 101.

In some implementations, the various playback devices, NMDs, and/orcontroller devices 102-104 may be communicatively coupled to at leastone remote computing device associated with a VAS and at least oneremote computing device associated with a media content service (“MCS”).For instance, in the illustrated example of FIG. 1B, remote computingdevices 106 are associated with a VAS 190 and remote computing devices106 b are associated with an MCS 192. Although only a single VAS 190 anda single MCS 192 are shown in the example of FIG. 1B for purposes ofclarity, the MPS 100 may be coupled to multiple, different VASes and/orMCSes. In some implementations, VASes may be operated by one or more ofAMAZON, GOOGLE, APPLE, MICROSOFT, SONOS or other voice assistantproviders. In some implementations, MCSes may be operated by one or moreof SPOTIFY, PANDORA, AMAZON MUSIC, or other media content services.

As further shown in FIG. 1B, the remote computing devices 106 furtherinclude remote computing device 106 c configured to perform certainoperations, such as remotely facilitating media playback functions,managing device and system status information, directing communicationsbetween the devices of the MPS 100 and one or multiple VASes and/orMCSes, among other operations. In one example, the remote computingdevices 106 c provide cloud servers for one or more SONOS Wireless HiFiSystems.

In various implementations, one or more of the playback devices 102 maytake the form of or include an on-board (e.g., integrated) networkmicrophone device. For example, the playback devices 102 a-e include orare otherwise equipped with corresponding NMDs 103 a-e, respectively. Aplayback device that includes or is equipped with an NMD may be referredto herein interchangeably as a playback device or an NMD unlessindicated otherwise in the description. In some cases, one or more ofthe NMDs 103 may be a stand-alone device. For example, the NMDs 103 fand 103 g may be stand-alone devices. A stand-alone NMD may omitcomponents and/or functionality that is typically included in a playbackdevice, such as a speaker or related electronics. For instance, in suchcases, a stand-alone NMD may not produce audio output or may producelimited audio output (e.g., relatively low-quality audio output).

The various playback and network microphone devices 102 and 103 of theMPS 100 may each be associated with a unique name, which may be assignedto the respective devices by a user, such as during setup of one or moreof these devices. For instance, as shown in the illustrated example ofFIG. 1B, a user may assign the name “Bookcase” to playback device 102 dbecause it is physically situated on a bookcase. Similarly, the NMD 103f may be assigned the named “Island” because it is physically situatedon an island countertop in the Kitchen 101 h (FIG. 1A). Some playbackdevices may be assigned names according to a zone or room, such as theplayback devices 102 e, 1021, 102 m, and 102 n, which are named“Bedroom,” “Dining Room,” “Living Room,” and “Office,” respectively.Further, certain playback devices may have functionally descriptivenames. For example, the playback devices 102 a and 102 b are assignedthe names “Right” and “Front,” respectively, because these two devicesare configured to provide specific audio channels during media playbackin the zone of the Den 101 d (FIG. 1A). The playback device 102 c in thePatio may be named portable because it is battery-powered and/or readilytransportable to different areas of the environment 101. Other namingconventions are possible.

As discussed above, an NMD may detect and process sound from itsenvironment, such as sound that includes background noise mixed withspeech spoken by a person in the NMD's vicinity. For example, as soundsare detected by the NMD in the environment, the NMD may process thedetected sound to determine if the sound includes speech that containsvoice input intended for the NMD and ultimately a particular VAS. Forexample, the NMD may identify whether speech includes a wake wordassociated with a particular VAS.

In the illustrated example of FIG. 1B, the NMDs 103 are configured tointeract with the VAS 190 over a network via the network 111 and therouter 109. Interactions with the VAS 190 may be initiated, for example,when an NMD identifies in the detected sound a potential wake word. Theidentification causes a wake-word event, which in turn causes the NMD tobegin transmitting detected-sound data to the VAS 190. In someimplementations, the various local network devices 102-105 (FIG. 1A)and/or remote computing devices 106 c of the MPS 100 may exchangevarious feedback, information, instructions, and/or related data withthe remote computing devices associated with the selected VAS. Suchexchanges may be related to or independent of transmitted messagescontaining voice inputs. In some embodiments, the remote computingdevice(s) and the MPS 100 may exchange data via communication paths asdescribed herein and/or using a metadata exchange channel as describedin U.S. application Ser. No. 15/438,749 filed Feb. 21, 2017, and titled“Voice Control of a Media Playback System,” which is herein incorporatedby reference in its entirety.

Upon receiving the stream of sound data, the VAS 190 determines if thereis voice input in the streamed data from the NMD, and if so the VAS 190will also determine an underlying intent in the voice input. The VAS 190may next transmit a response back to the MPS 100, which can includetransmitting the response directly to the NMD that caused the wake-wordevent. The response is typically based on the intent that the VAS 190determined was present in the voice input. As an example, in response tothe VAS 190 receiving a voice input with an utterance to “Play Hey Judeby The Beatles,” the VAS 190 may determine that the underlying intent ofthe voice input is to initiate playback and further determine thatintent of the voice input is to play the particular song “Hey Jude.”After these determinations, the VAS 190 may transmit a command to aparticular MCS 192 to retrieve content (i.e., the song “Hey Jude”), andthat MCS 192, in turn, provides (e.g., streams) this content directly tothe MPS 100 or indirectly via the VAS 190. In some implementations, theVAS 190 may transmit to the MPS 100 a command that causes the MPS 100itself to retrieve the content from the MCS 192.

In certain implementations, NMDs may facilitate arbitration amongst oneanother when voice input is identified in speech detected by two or moreNMDs located within proximity of one another. For example, theNMD-equipped playback device 102 d in the environment 101 (FIG. 1A) isin relatively close proximity to the NMD-equipped Living Room playbackdevice 102 m, and both devices 102 d and 102 m may at least sometimesdetect the same sound. In such cases, this may require arbitration as towhich device is ultimately responsible for providing detected-sound datato the remote VAS. Examples of arbitrating between NMDs may be found,for example, in previously referenced U.S. application Ser. No.15/438,749.

In certain implementations, an NMD may be assigned to, or otherwiseassociated with, a designated or default playback device that may notinclude an NMD. For example, the Island NMD 103 f in the Kitchen 101 h(FIG. 1A) may be assigned to the Dining Room playback device 102 l,which is in relatively close proximity to the Island NMD 103 f. Inpractice, an NMD may direct an assigned playback device to play audio inresponse to a remote VAS receiving a voice input from the NMD to playthe audio, which the NMD might have sent to the VAS in response to auser speaking a command to play a certain song, album, playlist, etc.Additional details regarding assigning NMDs and playback devices asdesignated or default devices may be found, for example, in previouslyreferenced U.S. patent application No.

Further aspects relating to the different components of the example MPS100 and how the different components may interact to provide a user witha media experience may be found in the following sections. Whilediscussions herein may generally refer to the example MPS 100,technologies described herein are not limited to applications within,among other things, the home environment described above. For instance,the technologies described herein may be useful in other homeenvironment configurations comprising more or fewer of any of theplayback, network microphone, and/or controller devices 102-104. Forexample, the technologies herein may be utilized within an environmenthaving a single playback device 102 and/or a single NMD 103. In someexamples of such cases, the NETWORK 111 (FIG. 1B) may be eliminated andthe single playback device 102 and/or the single NMD 103 may communicatedirectly with the remote computing devices 106-d. In some embodiments, atelecommunication network (e.g., an LTE network, a 5G network, etc.) maycommunicate with the various playback, network microphone, and/orcontroller devices 102-104 independent of a LAN.

a. Example Playback & Network Microphone Devices

FIG. 2A is a functional block diagram illustrating certain aspects ofone of the playback devices 102 of the MPS 100 of FIGS. 1A and 1B. Asshown, the playback device 102 includes various components, each ofwhich is discussed in further detail below, and the various componentsof the playback device 102 may be operably coupled to one another via asystem bus, communication network, or some other connection mechanism.In the illustrated example of FIG. 2A, the playback device 102 may bereferred to as an “NMD-equipped” playback device because it includescomponents that support the functionality of an NMD, such as one of theNMDs 103 shown in FIG. 1A.

As shown, the playback device 102 includes at least one processor 212,which may be a clock-driven computing component configured to processinput data according to instructions stored in memory 213. The memory213 may be a tangible, non-transitory, computer-readable mediumconfigured to store instructions that are executable by the processor212. For example, the memory 213 may be data storage that can be loadedwith software code 214 that is executable by the processor 212 toachieve certain functions.

In one example, these functions may involve the playback device 102retrieving audio data from an audio source, which may be anotherplayback device. In another example, the functions may involve theplayback device 102 sending audio data, detected-sound data (e.g.,corresponding to a voice input), and/or other information to anotherdevice on a network via at least one network interface 224. In yetanother example, the functions may involve the playback device 102causing one or more other playback devices to synchronously playbackaudio with the playback device 102. In yet a further example, thefunctions may involve the playback device 102 facilitating being pairedor otherwise bonded with one or more other playback devices to create amulti-channel audio environment. Numerous other example functions arepossible, some of which are discussed below.

As just mentioned, certain functions may involve the playback device 102synchronizing playback of audio content with one or more other playbackdevices. During synchronous playback, a listener may not perceivetime-delay differences between playback of the audio content by thesynchronized playback devices. U.S. Pat. No. 8,234,395 filed on Apr. 4,2004, and titled “System and method for synchronizing operations among aplurality of independently clocked digital data processing devices,”which is hereby incorporated by reference in its entirety, provides inmore detail some examples for audio playback synchronization amongplayback devices.

To facilitate audio playback, the playback device 102 includes audioprocessing components 216 that are generally configured to process audioprior to the playback device 102 rendering the audio. In this respect,the audio processing components 216 may include one or moredigital-to-analog converters (“DAC”), one or more audio preprocessingcomponents, one or more audio enhancement components, one or moredigital signal processors (“DSPs”), and so on. In some implementations,one or more of the audio processing components 216 may be a subcomponentof the processor 212. In operation, the audio processing components 216receive analog and/or digital audio and process and/or otherwiseintentionally alter the audio to produce audio signals for playback.

The produced audio signals may then be provided to one or more audioamplifiers 217 for amplification and playback through one or morespeakers 218 operably coupled to the amplifiers 217. The audioamplifiers 217 may include components configured to amplify audiosignals to a level for driving one or more of the speakers 218.

Each of the speakers 218 may include an individual transducer (e.g., a“driver”) or the speakers 218 may include a complete speaker systeminvolving an enclosure with one or more drivers. A particular driver ofa speaker 218 may include, for example, a subwoofer (e.g., for lowfrequencies), a mid-range driver (e.g., for middle frequencies), and/ora tweeter (e.g., for high frequencies). In some cases, a transducer maybe driven by an individual corresponding audio amplifier of the audioamplifiers 217. In some implementations, a playback device may notinclude the speakers 218, but instead may include a speaker interfacefor connecting the playback device to external speakers. In certainembodiments, a playback device may include neither the speakers 218 northe audio amplifiers 217, but instead may include an audio interface(not shown) for connecting the playback device to an external audioamplifier or audio-visual receiver.

In addition to producing audio signals for playback by the playbackdevice 102, the audio processing components 216 may be configured toprocess audio to be sent to one or more other playback devices, via thenetwork interface 224, for playback. In example scenarios, audio contentto be processed and/or played back by the playback device 102 may bereceived from an external source, such as via an audio line-in interface(e.g., an auto-detecting 3.5 mm audio line-in connection) of theplayback device 102 (not shown) or via the network interface 224, asdescribed below.

As shown, the at least one network interface 224, may take the form ofone or more wireless interfaces 225 and/or one or more wired interfaces226. A wireless interface may provide network interface functions forthe playback device 102 to wirelessly communicate with other devices(e.g., other playback device(s), NMD(s), and/or controller device(s)) inaccordance with a communication protocol (e.g., any wireless standardincluding IEEE 802.11a, 802.11b, 802.11g, 802.11n, 802.11ac, 802.15, 4Gmobile communication standard, and so on). A wired interface may providenetwork interface functions for the playback device 102 to communicateover a wired connection with other devices in accordance with acommunication protocol (e.g., IEEE 802.3). While the network interface224 shown in FIG. 2A include both wired and wireless interfaces, theplayback device 102 may in some implementations include only wirelessinterface(s) or only wired interface(s).

In general, the network interface 224 facilitates data flow between theplayback device 102 and one or more other devices on a data network. Forinstance, the playback device 102 may be configured to receive audiocontent over the data network from one or more other playback devices,network devices within a LAN, and/or audio content sources over a WAN,such as the Internet. In one example, the audio content and othersignals transmitted and received by the playback device 102 may betransmitted in the form of digital packet data comprising an InternetProtocol (IP)-based source address and IP-based destination addresses.In such a case, the network interface 224 may be configured to parse thedigital packet data such that the data destined for the playback device102 is properly received and processed by the playback device 102.

As shown in FIG. 2A, the playback device 102 also includes voiceprocessing components 220 that are operably coupled to one or moremicrophones 222. The microphones 222 are configured to detect sound(i.e., acoustic waves) in the environment of the playback device 102,which is then provided to the voice processing components 220. Morespecifically, each microphone 222 is configured to detect sound andconvert the sound into a digital or analog signal representative of thedetected sound, which can then cause the voice processing component 220to perform various functions based on the detected sound, as describedin greater detail below. In one implementation, the microphones 222 arearranged as an array of microphones (e.g., an array of six microphones).In some implementations, the playback device 102 includes more than sixmicrophones (e.g., eight microphones or twelve microphones) or fewerthan six microphones (e.g., four microphones, two microphones, or asingle microphones).

In operation, the voice-processing components 220 are generallyconfigured to detect and process sound received via the microphones 222,identify potential voice input in the detected sound, and extractdetected-sound data to enable a VAS, such as the VAS 190 (FIG. 1B), toprocess voice input identified in the detected-sound data. The voiceprocessing components 220 may include one or more analog-to-digitalconverters, an acoustic echo canceller (“AEC”), a spatial processor(e.g., one or more multi-channel Wiener filters, one or more otherfilters, and/or one or more beam former components), one or more buffers(e.g., one or more circular buffers), one or more wake-word engines, oneor more voice extractors, and/or one or more speech processingcomponents (e.g., components configured to recognize a voice of aparticular user or a particular set of users associated with ahousehold), among other example voice processing components. In exampleimplementations, the voice processing components 220 may include orotherwise take the form of one or more DSPs or one or more modules of aDSP. In this respect, certain voice processing components 220 may beconfigured with particular parameters (e.g., gain and/or spectralparameters) that may be modified or otherwise tuned to achieveparticular functions. In some implementations, one or more of the voiceprocessing components 220 may be a subcomponent of the processor 212.

As further shown in FIG. 2A, the playback device 102 also includes powercomponents 227. The power components 227 include at least an externalpower source interface 228, which may be coupled to a power source (notshown) via a power cable or the like that physically connects theplayback device 102 to an electrical outlet or some other external powersource. Other power components may include, for example, transformers,converters, and like components configured to format electrical power.

In some implementations, the power components 227 of the playback device102 may additionally include an internal power source 229 (e.g., one ormore batteries) configured to power the playback device 102 without aphysical connection to an external power source. When equipped with theinternal power source 229, the playback device 102 may operateindependent of an external power source. In some such implementations,the external power source interface 228 may be configured to facilitatecharging the internal power source 229. As discussed before, a playbackdevice comprising an internal power source may be referred to herein asa “portable playback device.” On the other hand, a playback device thatoperates using an external power source may be referred to herein as a“stationary playback device,” although such a device may in fact bemoved around a home or other environment.

The playback device 102 further includes a user interface 240 that mayfacilitate user interactions independent of or in conjunction with userinteractions facilitated by one or more of the controller devices 104.In various embodiments, the user interface 240 includes one or morephysical buttons and/or supports graphical interfaces provided on touchsensitive screen(s) and/or surface(s), among other possibilities, for auser to directly provide input. The user interface 240 may furtherinclude one or more of lights (e.g., LEDs) and the speakers to providevisual and/or audio feedback to a user.

As an illustrative example, FIG. 2B shows an example housing 230 of theplayback device 102 that includes a user interface in the form of acontrol area 232 at a top portion 234 of the housing 230. The controlarea 232 includes buttons 236 a-c for controlling audio playback, volumelevel, and other functions. The control area 232 also includes a button236 d for toggling the microphones 222 to either an on state or an offstate.

As further shown in FIG. 2B, the control area 232 is at least partiallysurrounded by apertures formed in the top portion 234 of the housing 230through which the microphones 222 (not visible in FIG. 2B) receive thesound in the environment of the playback device 102. The microphones 222may be arranged in various positions along and/or within the top portion234 or other areas of the housing 230 so as to detect sound from one ormore directions relative to the playback device 102.

By way of illustration, SONOS, Inc. presently offers (or has offered)for sale certain playback devices that may implement certain of theembodiments disclosed herein, including a “PLAY:1,” “PLAY:3,” “PLAY:5,”“PLAYBAR,” “CONNECT:AMP,” “PLAYBASE,” “BEAM,” “CONNECT,” and “SUB.” Anyother past, present, and/or future playback devices may additionally oralternatively be used to implement the playback devices of exampleembodiments disclosed herein. Additionally, it should be understood thata playback device is not limited to the examples illustrated in FIG. 2Aor 2B or to the SONOS product offerings. For example, a playback devicemay include, or otherwise take the form of, a wired or wirelessheadphone set, which may operate as a part of the MPS 100 via a networkinterface or the like. In another example, a playback device may includeor interact with a docking station for personal mobile media playbackdevices. In yet another example, a playback device may be integral toanother device or component such as a television, a lighting fixture, orsome other device for indoor or outdoor use.

FIG. 2C is a diagram of an example voice input 280 that may be processedby an NMD or an NMD-equipped playback device. The voice input 280 mayinclude a keyword portion 280 a and an utterance portion 280 b. Thekeyword portion 280 a may include a wake word or a command keyword. Inthe case of a wake word, the keyword portion 280 a corresponds todetected sound that caused a wake-word The utterance portion 280 bcorresponds to detected sound that potentially comprises a user requestfollowing the keyword portion 280 a. An utterance portion 280 b can beprocessed to identify the presence of any words in detected-sound databy the NMD in response to the event caused by the keyword portion 280 a.In various implementations, an underlying intent can be determined basedon the words in the utterance portion 280 b. In certain implementations,an underlying intent can also be based or at least partially based oncertain words in the keyword portion 280 a, such as when keyword portionincludes a command keyword. In any case, the words may correspond to oneor more commands, as well as a certain command and certain keywords. Akeyword in the voice utterance portion 280 b may be, for example, a wordidentifying a particular device or group in the MPS 100. For instance,in the illustrated example, the keywords in the voice utterance portion280 b may be one or more words identifying one or more zones in whichthe music is to be played, such as the Living Room and the Dining Room(FIG. 1A). In some cases, the utterance portion 280 b may includeadditional information, such as detected pauses (e.g., periods ofnon-speech) between words spoken by a user, as shown in FIG. 2C. Thepauses may demarcate the locations of separate commands, keywords, orother information spoke by the user within the utterance portion 280 b.

Based on certain command criteria, the NMD and/or a remote VAS may takeactions as a result of identifying one or more commands in the voiceinput. Command criteria may be based on the inclusion of certainkeywords within the voice input, among other possibilities.Additionally, or alternatively, command criteria for commands mayinvolve identification of one or more control-state and/or zone-statevariables in conjunction with identification of one or more particularcommands. Control-state variables may include, for example, indicatorsidentifying a level of volume, a queue associated with one or moredevices, and playback state, such as whether devices are playing aqueue, paused, etc. Zone-state variables may include, for example,indicators identifying which, if any, zone players are grouped.

In some implementations, the MPS 100 is configured to temporarily reducethe volume of audio content that it is playing upon detecting a certainkeyword, such as a wake word, in the keyword portion 280 a. The MPS 100may restore the volume after processing the voice input 280. Such aprocess can be referred to as ducking, examples of which are disclosedin U.S. patent application Ser. No. 15/438,749, incorporated byreference herein in its entirety.

FIG. 2D shows an example sound specimen. In this example, the soundspecimen corresponds to the sound-data stream (e.g., one or more audioframes) associated with a spotted wake word or command keyword in thekeyword portion 280 a of FIG. 2A. As illustrated, the example soundspecimen comprises sound detected in an NMD's environment (i)immediately before a wake or command word was spoken, which may bereferred to as a pre-roll portion (between times t₀ and t₁), (ii) whilea wake or command word was spoken, which may be referred to as awake-meter portion (between times t₁ and t₂), and/or (iii) after thewake or command word was spoken, which may be referred to as a post-rollportion (between times t₂ and t₃). Other sound specimens are alsopossible. In various implementations, aspects of the sound specimen canbe evaluated according to an acoustic model which aims to mapmels/spectral features to phonemes in a given language model for furtherprocessing. For example, automatic speech recognition (ASR) may includesuch mapping for command-keyword detection. Wake-word detection engines,by contrast, may be precisely tuned to identify a specific wake-word,and a downstream action of invoking a VAS (e.g., by targeting only noncewords in the voice input processed by the playback device).

ASR for command keyword detection may be tuned to accommodate a widerange of keywords (e.g., 5, 10, 100, 1,000, 10,000 keywords). Commandkeyword detection, in contrast to wake-word detection, may involvefeeding ASR output to an onboard, local NLU which together with the ASRdetermine when command word events have occurred. In someimplementations described below, the local NLU may determine an intentbased on one or more other keywords in the ASR output produced by aparticular voice input. In these or other implementations, a playbackdevice may act on a detected command keyword event only when theplayback devices determines that certain conditions have been met, suchas environmental conditions (e.g., low background noise).

b. Example Playback Device Configurations

FIGS. 3A-3E show example configurations of playback devices. Referringfirst to FIG. 3A, in some example instances, a single playback devicemay belong to a zone. For example, the playback device 102 c (FIG. 1A)on the Patio may belong to Zone A. In some implementations describedbelow, multiple playback devices may be “bonded” to form a “bondedpair,” which together form a single zone. For example, the playbackdevice 102 f (FIG. 1A) named “Bed 1” in FIG. 3A may be bonded to theplayback device 102 g (FIG. 1A) named “Bed 2” in FIG. 3A to form Zone B.Bonded playback devices may have different playback responsibilities(e.g., channel responsibilities). In another implementation describedbelow, multiple playback devices may be merged to form a single zone.For example, the playback device 102 d named “Bookcase” may be mergedwith the playback device 102 m named “Living Room” to form a single ZoneC. The merged playback devices 102 d and 102 m may not be specificallyassigned different playback responsibilities. That is, the mergedplayback devices 102 d and 102 m may, aside from playing audio contentin synchrony, each play audio content as they would if they were notmerged.

For purposes of control, each zone in the MPS 100 may be represented asa single user interface (“UI”) entity. For example, as displayed by thecontroller devices 104, Zone A may be provided as a single entity named“Portable,” Zone B may be provided as a single entity named “Stereo,”and Zone C may be provided as a single entity named “Living Room.”

In various embodiments, a zone may take on the name of one of theplayback devices belonging to the zone. For example, Zone C may take onthe name of the Living Room device 102 m (as shown). In another example,Zone C may instead take on the name of the Bookcase device 102 d. In afurther example, Zone C may take on a name that is some combination ofthe Bookcase device 102 d and Living Room device 102 m. The name that ischosen may be selected by a user via inputs at a controller device 104.In some embodiments, a zone may be given a name that is different thanthe device(s) belonging to the zone. For example, Zone B in FIG. 3A isnamed “Stereo” but none of the devices in Zone B have this name. In oneaspect, Zone B is a single UI entity representing a single device named“Stereo,” composed of constituent devices “Bed 1” and “Bed 2.” In oneimplementation, the Bed 1 device may be playback device 102 f in themaster bedroom 101 h (FIG. 1A) and the Bed 2 device may be the playbackdevice 102 g also in the master bedroom 101 h (FIG. 1A).

As noted above, playback devices that are bonded may have differentplayback responsibilities, such as playback responsibilities for certainaudio channels. For example, as shown in FIG. 3B, the Bed 1 and Bed 2devices 102 f and 102 g may be bonded so as to produce or enhance astereo effect of audio content. In this example, the Bed 1 playbackdevice 102 f may be configured to play a left channel audio component,while the Bed 2 playback device 102 g may be configured to play a rightchannel audio component. In some implementations, such stereo bondingmay be referred to as “pairing.”

Additionally, playback devices that are configured to be bonded may haveadditional and/or different respective speaker drivers. As shown in FIG.3C, the playback device 102 b named “Front” may be bonded with theplayback device 102 k named “SUB.” The Front device 102 b may render arange of mid to high frequencies, and the SUB device 102 k may renderlow frequencies as, for example, a subwoofer. When unbonded, the Frontdevice 102 b may be configured to render a full range of frequencies. Asanother example, FIG. 3D shows the Front and SUB devices 102 b and 102 kfurther bonded with Right and Left playback devices 102 a and 102 j,respectively. In some implementations, the Right and Left devices 102 aand 102 j may form surround or “satellite” channels of a home theatersystem. The bonded playback devices 102 a, 102 b, 102 j, and 102 k mayform a single Zone D (FIG. 3A).

In some implementations, playback devices may also be “merged.” Incontrast to certain bonded playback devices, playback devices that aremerged may not have assigned playback responsibilities, but may eachrender the full range of audio content that each respective playbackdevice is capable of. Nevertheless, merged devices may be represented asa single UI entity (i.e., a zone, as discussed above). For instance,FIG. 3E shows the playback devices 102 d and 102 m in the Living Roommerged, which would result in these devices being represented by thesingle UI entity of Zone C. In one embodiment, the playback devices 102d and 102 m may playback audio in synchrony, during which each outputsthe full range of audio content that each respective playback device 102d and 102 m is capable of rendering.

In some embodiments, a stand-alone NMD may be in a zone by itself. Forexample, the NMD 103 h from FIG. 1A is named “Closet” and forms Zone Iin FIG. 3A. An NMD may also be bonded or merged with another device soas to form a zone. For example, the NMD device 103 f named “Island” maybe bonded with the playback device 102 i Kitchen, which together formZone F, which is also named “Kitchen.” Additional details regardingassigning NMDs and playback devices as designated or default devices maybe found, for example, in previously referenced U.S. patent applicationSer. No. 15/438,749. In some embodiments, a stand-alone NMD may not beassigned to a zone.

Zones of individual, bonded, and/or merged devices may be arranged toform a set of playback devices that playback audio in synchrony. Such aset of playback devices may be referred to as a “group,” “zone group,”“synchrony group,” or “playback group.” In response to inputs providedvia a controller device 104, playback devices may be dynamically groupedand ungrouped to form new or different groups that synchronously playback audio content. For example, referring to FIG. 3A, Zone A may begrouped with Zone B to form a zone group that includes the playbackdevices of the two zones. As another example, Zone A may be grouped withone or more other Zones C-I. The Zones A-I may be grouped and ungroupedin numerous ways. For example, three, four, five, or more (e.g., all) ofthe Zones A-I may be grouped. When grouped, the zones of individualand/or bonded playback devices may play back audio in synchrony with oneanother, as described in previously referenced U.S. Pat. No. 8,234,395.Grouped and bonded devices are example types of associations betweenportable and stationary playback devices that may be caused in responseto a trigger event, as discussed above and described in greater detailbelow.

In various implementations, the zones in an environment may be assigneda particular name, which may be the default name of a zone within a zonegroup or a combination of the names of the zones within a zone group,such as “Dining Room+Kitchen,” as shown in FIG. 3A. In some embodiments,a zone group may be given a unique name selected by a user, such as“Nick's Room,” as also shown in FIG. 3A. The name “Nick's Room” may be aname chosen by a user over a prior name for the zone group, such as theroom name “Master Bedroom.”

Referring back to FIG. 2A, certain data may be stored in the memory 213as one or more state variables that are periodically updated and used todescribe the state of a playback zone, the playback device(s), and/or azone group associated therewith. The memory 213 may also include thedata associated with the state of the other devices of the MPS 100,which may be shared from time to time among the devices so that one ormore of the devices have the most recent data associated with thesystem.

In some embodiments, the memory 213 of the playback device 102 may storeinstances of various variable types associated with the states.Variables instances may be stored with identifiers (e.g., tags)corresponding to type. For example, certain identifiers may be a firsttype “a1” to identify playback device(s) of a zone, a second type “b1”to identify playback device(s) that may be bonded in the zone, and athird type “c1” to identify a zone group to which the zone may belong.As a related example, in FIG. 1A, identifiers associated with the Patiomay indicate that the Patio is the only playback device of a particularzone and not in a zone group. Identifiers associated with the LivingRoom may indicate that the Living Room is not grouped with other zonesbut includes bonded playback devices 102 a, 102 b, 102 j, and 102 k.Identifiers associated with the Dining Room may indicate that the DiningRoom is part of Dining Room+Kitchen group and that devices 103 f and 102i are bonded. Identifiers associated with the Kitchen may indicate thesame or similar information by virtue of the Kitchen being part of theDining Room+Kitchen zone group. Other example zone variables andidentifiers are described below.

In yet another example, the MPS 100 may include variables or identifiersrepresenting other associations of zones and zone groups, such asidentifiers associated with Areas, as shown in FIG. 3A. An Area mayinvolve a cluster of zone groups and/or zones not within a zone group.For instance, FIG. 3A shows a first area named “First Area” and a secondarea named “Second Area.” The First Area includes zones and zone groupsof the Patio, Den, Dining Room, Kitchen, and Bathroom. The Second Areaincludes zones and zone groups of the Bathroom, Nick's Room, Bedroom,and Living Room. In one aspect, an Area may be used to invoke a clusterof zone groups and/or zones that share one or more zones and/or zonegroups of another cluster. In this respect, such an Area differs from azone group, which does not share a zone with another zone group. Furtherexamples of techniques for implementing Areas may be found, for example,in U.S. application Ser. No. 15/682,506 filed Aug. 21, 2017 and titled“Room Association Based on Name,” and U.S. Pat. No. 8,483,853 filed Sep.11, 2007, and titled “Controlling and manipulating groupings in amulti-zone media system.” Each of these applications is incorporatedherein by reference in its entirety. In some embodiments, the MPS 100may not implement Areas, in which case the system may not storevariables associated with Areas.

The memory 213 may be further configured to store other data. Such datamay pertain to audio sources accessible by the playback device 102 or aplayback queue that the playback device (or some other playbackdevice(s)) may be associated with. In embodiments described below, thememory 213 is configured to store a set of command data for selecting aparticular VAS when processing voice inputs. During operation, one ormore playback zones in the environment of FIG. 1A may each be playingdifferent audio content. For instance, the user may be grilling in thePatio zone and listening to hip hop music being played by the playbackdevice 102 c, while another user may be preparing food in the Kitchenzone and listening to classical music being played by the playbackdevice 102 i. In another example, a playback zone may play the sameaudio content in synchrony with another playback zone.

For instance, the user may be in the Office zone where the playbackdevice 102 n is playing the same hip-hop music that is being playing byplayback device 102 c in the Patio zone. In such a case, playbackdevices 102 c and 102 n may be playing the hip-hop in synchrony suchthat the user may seamlessly (or at least substantially seamlessly)enjoy the audio content that is being played out-loud while movingbetween different playback zones. Synchronization among playback zonesmay be achieved in a manner similar to that of synchronization amongplayback devices, as described in previously referenced U.S. Pat. No.8,234,395.

As suggested above, the zone configurations of the MPS 100 may bedynamically modified. As such, the MPS 100 may support numerousconfigurations. For example, if a user physically moves one or moreplayback devices to or from a zone, the MPS 100 may be reconfigured toaccommodate the change(s). For instance, if the user physically movesthe playback device 102 c from the Patio zone to the Office zone, theOffice zone may now include both the playback devices 102 c and 102 n.In some cases, the user may pair or group the moved playback device 102c with the Office zone and/or rename the players in the Office zoneusing, for example, one of the controller devices 104 and/or voiceinput. As another example, if one or more playback devices 102 are movedto a particular space in the home environment that is not already aplayback zone, the moved playback device(s) may be renamed or associatedwith a playback zone for the particular space.

Further, different playback zones of the MPS 100 may be dynamicallycombined into zone groups or split up into individual playback zones.For example, the Dining Room zone and the Kitchen zone may be combinedinto a zone group for a dinner party such that playback devices 102 iand 102 l may render audio content in synchrony. As another example,bonded playback devices in the Den zone may be split into (i) atelevision zone and (ii) a separate listening zone. The television zonemay include the Front playback device 102 b. The listening zone mayinclude the Right, Left, and SUB playback devices 102 a, 102 j, and 102k, which may be grouped, paired, or merged, as described above.Splitting the Den zone in such a manner may allow one user to listen tomusic in the listening zone in one area of the living room space, andanother user to watch the television in another area of the living roomspace. In a related example, a user may utilize either of the NMD 103 aor 103 b (FIG. 1B) to control the Den zone before it is separated intothe television zone and the listening zone. Once separated, thelistening zone may be controlled, for example, by a user in the vicinityof the NMD 103 a, and the television zone may be controlled, forexample, by a user in the vicinity of the NMD 103 b. As described above,however, any of the NMDs 103 may be configured to control the variousplayback and other devices of the MPS 100.

c. Example Controller Devices

FIG. 4 is a functional block diagram illustrating certain aspects of aselected one of the controller devices 104 of the MPS 100 of FIG. 1A.Such controller devices may also be referred to herein as a “controldevice” or “controller.” The controller device shown in FIG. 4 mayinclude components that are generally similar to certain components ofthe network devices described above, such as a processor 412, memory 413storing program software 414, at least one network interface 424, andone or more microphones 422. In one example, a controller device may bea dedicated controller for the MPS 100. In another example, a controllerdevice may be a network device on which media playback system controllerapplication software may be installed, such as for example, an iPhone™,iPad™ or any other smart phone, tablet, or network device (e.g., anetworked computer such as a PC or Mac™).

The memory 413 of the controller device 104 may be configured to storecontroller application software and other data associated with the MPS100 and/or a user of the system 100. The memory 413 may be loaded withinstructions in software 414 that are executable by the processor 412 toachieve certain functions, such as facilitating user access, control,and/or configuration of the MPS 100. The controller device 104 isconfigured to communicate with other network devices via the networkinterface 424, which may take the form of a wireless interface, asdescribed above.

In one example, system information (e.g., such as a state variable) maybe communicated between the controller device 104 and other devices viathe network interface 424. For instance, the controller device 104 mayreceive playback zone and zone group configurations in the MPS 100 froma playback device, an NMD, or another network device. Likewise, thecontroller device 104 may transmit such system information to a playbackdevice or another network device via the network interface 424. In somecases, the other network device may be another controller device.

The controller device 104 may also communicate playback device controlcommands, such as volume control and audio playback control, to aplayback device via the network interface 424. As suggested above,changes to configurations of the MPS 100 may also be performed by a userusing the controller device 104. The configuration changes may includeadding/removing one or more playback devices to/from a zone,adding/removing one or more zones to/from a zone group, forming a bondedor merged player, separating one or more playback devices from a bondedor merged player, among others.

As shown in FIG. 4 , the controller device 104 also includes a userinterface 440 that is generally configured to facilitate user access andcontrol of the MPS 100. The user interface 440 may include atouch-screen display or other physical interface configured to providevarious graphical controller interfaces, such as the controllerinterfaces 540 a and 540 b shown in FIGS. 5A and 5B. Referring to FIGS.5A and 5B together, the controller interfaces 540 a and 540 b includes aplayback control region 542, a playback zone region 543, a playbackstatus region 544, a playback queue region 546, and a sources region548. The user interface as shown is just one example of an interfacethat may be provided on a network device, such as the controller deviceshown in FIG. 4 , and accessed by users to control a media playbacksystem, such as the MPS 100. Other user interfaces of varying formats,styles, and interactive sequences may alternatively be implemented onone or more network devices to provide comparable control access to amedia playback system.

The playback control region 542 (FIG. 5A) may include selectable icons(e.g., by way of touch or by using a cursor) that, when selected, causeplayback devices in a selected playback zone or zone group to play orpause, fast forward, rewind, skip to next, skip to previous, enter/exitshuffle mode, enter/exit repeat mode, enter/exit cross fade mode, etc.The playback control region 542 may also include selectable icons that,when selected, modify equalization settings and/or playback volume,among other possibilities.

The playback zone region 543 (FIG. 5B) may include representations ofplayback zones within the MPS 100. The playback zones regions 543 mayalso include a representation of zone groups, such as the DiningRoom+Kitchen zone group, as shown.

In some embodiments, the graphical representations of playback zones maybe selectable to bring up additional selectable icons to manage orconfigure the playback zones in the MPS 100, such as a creation ofbonded zones, creation of zone groups, separation of zone groups, andrenaming of zone groups, among other possibilities.

For example, as shown, a “group” icon may be provided within each of thegraphical representations of playback zones. The “group” icon providedwithin a graphical representation of a particular zone may be selectableto bring up options to select one or more other zones in the MPS 100 tobe grouped with the particular zone. Once grouped, playback devices inthe zones that have been grouped with the particular zone will beconfigured to play audio content in synchrony with the playbackdevice(s) in the particular zone. Analogously, a “group” icon may beprovided within a graphical representation of a zone group. In thiscase, the “group” icon may be selectable to bring up options to deselectone or more zones in the zone group to be removed from the zone group.Other interactions and implementations for grouping and ungrouping zonesvia a user interface are also possible. The representations of playbackzones in the playback zone region 543 (FIG. 5B) may be dynamicallyupdated as playback zone or zone group configurations are modified.

The playback status region 544 (FIG. 5A) may include graphicalrepresentations of audio content that is presently being played,previously played, or scheduled to play next in the selected playbackzone or zone group. The selected playback zone or zone group may bevisually distinguished on a controller interface, such as within theplayback zone region 543 and/or the playback status region 544. Thegraphical representations may include track title, artist name, albumname, album year, track length, and/or other relevant information thatmay be useful for the user to know when controlling the MPS 100 via acontroller interface.

The playback queue region 546 may include graphical representations ofaudio content in a playback queue associated with the selected playbackzone or zone group. In some embodiments, each playback zone or zonegroup may be associated with a playback queue comprising informationcorresponding to zero or more audio items for playback by the playbackzone or zone group. For instance, each audio item in the playback queuemay comprise a uniform resource identifier (URI), a uniform resourcelocator (URL), or some other identifier that may be used by a playbackdevice in the playback zone or zone group to find and/or retrieve theaudio item from a local audio content source or a networked audiocontent source, which may then be played back by the playback device.

In one example, a playlist may be added to a playback queue, in whichcase information corresponding to each audio item in the playlist may beadded to the playback queue. In another example, audio items in aplayback queue may be saved as a playlist. In a further example, aplayback queue may be empty, or populated but “not in use” when theplayback zone or zone group is playing continuously streamed audiocontent, such as Internet radio that may continue to play untilotherwise stopped, rather than discrete audio items that have playbackdurations. In an alternative embodiment, a playback queue can includeInternet radio and/or other streaming audio content items and be “inuse” when the playback zone or zone group is playing those items. Otherexamples are also possible.

When playback zones or zone groups are “grouped” or “ungrouped,”playback queues associated with the affected playback zones or zonegroups may be cleared or re-associated. For example, if a first playbackzone including a first playback queue is grouped with a second playbackzone including a second playback queue, the established zone group mayhave an associated playback queue that is initially empty, that containsaudio items from the first playback queue (such as if the secondplayback zone was added to the first playback zone), that contains audioitems from the second playback queue (such as if the first playback zonewas added to the second playback zone), or a combination of audio itemsfrom both the first and second playback queues. Subsequently, if theestablished zone group is ungrouped, the resulting first playback zonemay be re-associated with the previous first playback queue or may beassociated with a new playback queue that is empty or contains audioitems from the playback queue associated with the established zone groupbefore the established zone group was ungrouped. Similarly, theresulting second playback zone may be re-associated with the previoussecond playback queue or may be associated with a new playback queuethat is empty or contains audio items from the playback queue associatedwith the established zone group before the established zone group wasungrouped. Other examples are also possible.

With reference still to FIGS. 5A and 5B, the graphical representationsof audio content in the playback queue region 646 (FIG. 5A) may includetrack titles, artist names, track lengths, and/or other relevantinformation associated with the audio content in the playback queue. Inone example, graphical representations of audio content may beselectable to bring up additional selectable icons to manage and/ormanipulate the playback queue and/or audio content represented in theplayback queue. For instance, a represented audio content may be removedfrom the playback queue, moved to a different position within theplayback queue, or selected to be played immediately, or after anycurrently playing audio content, among other possibilities. A playbackqueue associated with a playback zone or zone group may be stored in amemory on one or more playback devices in the playback zone or zonegroup, on a playback device that is not in the playback zone or zonegroup, and/or some other designated device. Playback of such a playbackqueue may involve one or more playback devices playing back media itemsof the queue, perhaps in sequential or random order.

The sources region 548 may include graphical representations ofselectable audio content sources and/or selectable voice assistantsassociated with a corresponding VAS. The VASes may be selectivelyassigned. In some examples, multiple VASes, such as AMAZON's Alexa,MICROSOFT's Cortana, etc., may be invokable by the same NMD. In someembodiments, a user may assign a VAS exclusively to one or more NMDs.For example, a user may assign a first VAS to one or both of the NMDs102 a and 102 b in the Living Room shown in FIG. 1A, and a second VAS tothe NMD 103 f in the Kitchen. Other examples are possible.

d. Example Audio Content Sources

The audio sources in the sources region 548 may be audio content sourcesfrom which audio content may be retrieved and played by the selectedplayback zone or zone group. One or more playback devices in a zone orzone group may be configured to retrieve for playback audio content(e.g., according to a corresponding URI or URL for the audio content)from a variety of available audio content sources. In one example, audiocontent may be retrieved by a playback device directly from acorresponding audio content source (e.g., via a line-in connection). Inanother example, audio content may be provided to a playback device overa network via one or more other playback devices or network devices. Asdescribed in greater detail below, in some embodiments audio content maybe provided by one or more media content services.

Example audio content sources may include a memory of one or moreplayback devices in a media playback system such as the MPS 100 of FIG.1 , local music libraries on one or more network devices (e.g., acontroller device, a network-enabled personal computer, or anetworked-attached storage (“NAS”)), streaming audio services providingaudio content via the Internet (e.g., cloud-based music services), oraudio sources connected to the media playback system via a line-in inputconnection on a playback device or network device, among otherpossibilities.

In some embodiments, audio content sources may be added or removed froma media playback system such as the MPS 100 of FIG. 1A. In one example,an indexing of audio items may be performed whenever one or more audiocontent sources are added, removed, or updated. Indexing of audio itemsmay involve scanning for identifiable audio items in allfolders/directories shared over a network accessible by playback devicesin the media playback system and generating or updating an audio contentdatabase comprising metadata (e.g., title, artist, album, track length,among others) and other associated information, such as a URI or URL foreach identifiable audio item found. Other examples for managing andmaintaining audio content sources may also be possible.

FIG. 6 is a message flow diagram illustrating data exchanges betweendevices of the MPS 100. At step 650 a, the MPS 100 receives anindication of selected media content (e.g., one or more songs, albums,playlists, podcasts, videos, stations) via the control device 104. Theselected media content can comprise, for example, media items storedlocally on or more devices connected to the media playback system and/ormedia items stored on one or more media service servers (one or more ofthe remote computing devices 106 of FIG. 1B). In response to receivingthe indication of the selected media content, the control device 104transmits a message 651 a to the playback device 102 (FIGS. 1A-1B) toadd the selected media content to a playback queue on the playbackdevice 102.

At step 650 b, the playback device 102 receives the message 651 a andadds the selected media content to the playback queue for play back.

At step 650 c, the control device 104 receives input corresponding to acommand to play back the selected media content. In response toreceiving the input corresponding to the command to play back theselected media content, the control device 104 transmits a message 651 bto the playback device 102 causing the playback device 102 to play backthe selected media content. In response to receiving the message 651 b,the playback device 102 transmits a message 651 c to the computingdevice 106 requesting the selected media content. The computing device106, in response to receiving the message 651 c, transmits a message 651d comprising data (e.g., audio data, video data, a URL, a URI)corresponding to the requested media content.

At step 650 d, the playback device 102 receives the message 651 d withthe data corresponding to the requested media content and plays back theassociated media content.

At step 650 e, the playback device 102 optionally causes one or moreother devices to play back the selected media content. In one example,the playback device 102 is one of a bonded zone of two or more players.The playback device 102 can receive the selected media content andtransmit all or a portion of the media content to other devices in thebonded zone. In another example, the playback device 102 is acoordinator of a group and is configured to transmit and receive timinginformation from one or more other devices in the group. The other oneor more devices in the group can receive the selected media content fromthe computing device 106, and begin playback of the selected mediacontent in response to a message from the playback device 102 such thatall of the devices in the group play back the selected media content insynchrony.

III. Example Network Microphone Device

FIG. 7A is a functional block diagram illustrating certain aspects of anexample network microphone device (NMD) 703. Generally, the NMD 703 maybe similar to the network microphone device(s) 103 illustrated in FIGS.1A and 1B. As shown, the NMD 703 includes various components, each ofwhich is discussed in further detail below. Many of these components aresimilar to the playback device 102 of FIG. 2A. In contrast to theNMD-equipped playback device of FIG. 2A, however, the NMD 703 is notdesigned for audio content playback and therefore may exclude audioprocessing components 216, amplifiers 217, and/or speakers 218 or mayinclude relatively less capable versions of these components. Thevarious components of the NMD 703 may be operably coupled to one anothervia a system bus, communication network, or some other connectionmechanism.

As shown, the NMD 703 includes at least one processor 712, which may bea clock-driven computing component configured to process input dataaccording to instructions stored in memory 713. The memory 713 may be atangible, non-transitory, computer-readable medium configured to storeinstructions that are executable by the processor 712. For example, thememory 713 may be data storage that can be loaded with software code 714that is executable by the processor 712 to achieve certain functions.

The at least one network interface 724 may take the form of one or morewireless interfaces 725 and/or one or more wired interfaces 726. Thewireless interface 725 may provide network interface functions for theNMD 703 to wirelessly communicate with other devices (e.g., playbackdevice(s) 102, other NMD(s) 103, and/or controller device(s) 104) inaccordance with a communication protocol (e.g., any wireless standardincluding IEEE 802.11a, 802.11b, 802.11g, 802.11n, 802.11ac, 802.15, 4Gmobile communication standard, and so on). The wired interface 726 mayprovide network interface functions for the NMD 703 to communicate overa wired connection with other devices in accordance with a communicationprotocol (e.g., IEEE 802.3). While the network interface 724 shown inFIG. 7A includes both wired and wireless interfaces, the playback device102 may in various implementations include only wireless interface(s) oronly wired interface(s).

As shown in FIG. 7A, the NMD 703 also includes voice processingcomponents 720 that are operably coupled to microphones 722. Themicrophones 722 are configured to detect sound (i.e., acoustic waves) inthe environment of the NMD 703, which is then provided to the voiceprocessing components 720. More specifically, the microphones 722 areconfigured to detect sound and convert the sound into a digital oranalog signal representative of the detected sound, which can then causethe voice processing component 720 to perform various functions based onthe detected sound, as described in greater detail below. In oneimplementation, the microphones 722 are arranged as one or more arraysof microphones (e.g., an array of six microphones). In someimplementations, the NMD 703 includes more than six microphones (e.g.,eight microphones or twelve microphones) or fewer than six microphones(e.g., four microphones, two microphones, or a single microphone).

In operation, similar to the voice-processing components 220 of theNMD-equipped playback device 102 the voice-processing components 720 aregenerally configured to detect and process sound received via themicrophones 722, identify potential voice input in the detected sound,and extract detected-sound data to enable processing of the voice inputby a cloud-based VAS, such as the VAS 190 (FIG. 1B), or a local NLU. Thevoice processing components 720 may include one or moreanalog-to-digital converters, an acoustic echo canceller (“AEC”), aspatial processor, one or more buffers (e.g., one or more circularbuffers), one or more wake-word engines, one or more voice extractors,and/or one or more speech processing components (e.g., componentsconfigured to recognize a voice of a particular user or a particular setof users associated with a household), among other example voiceprocessing components. In example implementations, the voice processingcomponents 720 may include or otherwise take the form of one or moreDSPs or one or more modules of a DSP. In some implementations, one ormore of the voice processing components 720 may be a subcomponent of theprocessor 712.

The NMD 703 also includes one or more orientation sensors 723 configuredto detect an orientation of the NMD 703. The orientation sensor(s) 723may include one or more accelerometers, one or more gyroscopes, and/or amagnetometer facilitate detecting an orientation of the NMD 703. Variousimplementations may implement any suitable orientation detectiontechniques.

As further shown in FIG. 2A, the NMD 703 also includes power components727. The power components 727 include at least an external power sourceinterface 728, which may be coupled to a power source (not shown) via apower cable or the like that physically connects the NMD 703 to anelectrical outlet or some other external power source. Other powercomponents may include, for example, transformers, converters, and likecomponents configured to format electrical power.

In some implementations, the power components 727 of the NMD 703 mayadditionally include an internal power source 729 (e.g., one or morebatteries) configured to power the NMD 703 without a physical connectionto an external power source. When equipped with the internal powersource 729, the NMD 703 may operate independent of an external powersource. In some such implementations, the external power sourceinterface 728 may be configured to facilitate charging the internalpower source 729. As discussed before, a NMD comprising an internalpower source may be referred to herein as a “portable NMD.” On the otherhand, a NMD that operates using an external power source may be referredto herein as a “stationary NMD,” although such a device may in fact bemoved around a home or other environment (e.g., to be connected todifferent power outlets of a home or other building).

The NMD 703 further includes a user interface 740 that may facilitateuser interactions independent of or in conjunction with userinteractions facilitated by one or more of the controller devices 104.In various embodiments, the user interface 740 includes one or morephysical buttons and/or supports graphical interfaces provided on touchsensitive screen(s) and/or surface(s), among other possibilities, for auser to directly provide input. The user interface 740 may furtherinclude one or more of lights (e.g., LEDs) and the speakers to providevisual and/or audio feedback to a user.

As an illustrative example, FIG. 7B shows an example housing 730 of theNMD 703 in a first orientation with a top portion 734 a of the housing730 oriented upwards. The top portion 734 a of the housing 730 includesa user interface 740 a carried on the top portion 734 a of the housing730. The user interface 740 a includes buttons 736 a-736 c forcontrolling audio playback, volume level, and other functions. The userinterface 740 a also includes a button 736 d for toggling themicrophones 722 a to either an on state or an off state.

As further shown in FIG. 7B, apertures are formed in the top portion 734a of the housing 730 through which one or more first microphones 722 areceive sound in the environment of the NMD 703. The microphones 722 amay be arranged in various positions along and/or within the top portion734 a or other areas of the housing 730 so as to detect sound from oneor more directions relative to the NMD 703.

FIG. 7C shows the example housing 730 of the NMD 703 in a secondorientation with a bottom portion 734 b of the housing 730 orientedupwards. Similar to the top portion 734 a, the bottom portion 734 b ofthe housing 730 includes a user interface 740 b carried on the bottomportion 734 a of the housing 730. The user interface 740 b includesbuttons 736 a′-736 c′ for controlling audio playback, volume level, andother functions. The user interface 740 b also includes a button 736 d′for toggling the microphones 722 b to either an on state or an offstate.

Similar to the top portion 734 a, apertures are formed in the bottomportion 734 b of the housing 730 through which one or more secondmicrophones 722 b receive sound in the environment of the NMD 703. Themicrophones 722 b may be arranged in various positions along and/orwithin the bottom portion 734 b or other areas of the housing 730 so asto detect sound from one or more directions relative to the NMD 703.

FIG. 7D illustrates the NMD 703 being re-oriented from the firstorientation to the second orientation by flipping over the housing 730.In operation, the orientation sensor(s) 723 detect that the housing 730is in the first orientation or the second orientation. When theorientation sensor(s) 723 detect that the housing 730 is in the firstorientation, the NMD 703 enables a first mode associated with localprocessing of voice inputs detected via the microphone(s) 722 a.Conversely, when the orientation sensor(s) 723 detect that the housing730 is in the second orientation, the NMD 703 enables a second modeassociated with cloud processing of voice inputs detected via themicrophone(s) 722 b.

More particularly, in the first mode, voice input processing viacloud-based voice assistant services is disabled. Instead, voice inputsare processed locally on via a local natural language unit. Since voiceinputs are not sent to any cloud-based VAS in the first mode, operationin the first mode may enhance user privacy.

In contrast, in the second mode, voice input processing via cloud-basedvoice assistant services is enabled. In this mode, voice inputs directedto a cloud-based VAS (e.g., via a VAS wake word) are send to thecloud-based VAS for processing. This second mode allows the user to takeadvantage of the relatively-greater capabilities of cloud-based voiceassistant services relative to processing via a local NLU. At the sametime, in some implementations, the local NLU remains enabled in thesecond mode, which allows users to direct certain voice inputs for localprocessing (e.g., via a local wake word).

In various examples, the top portion 734 a and bottom portion of the 734b may be implemented using different colors, patterns, textures, orother visual differences. Visual differences between the top portion 734a and bottom portion of the 734 b of the housing 730 may assist a userin determining whether the NMD 703 is operating in the first mode (withthe top portion 734 a facing upwards) or operating in the second mode(with the bottom portion 734 b facing upwards), especially from across aroom.

Within example implementations, enabling the first mode or the secondmode may involve enabling or disabling the microphones 722. Inparticular, while the NMD 730 is in the first orientation, themicrophones 722 a are enabled and the microphones 722 b are disabled.Conversely, while the NMD 730 is operating in the second mode, themicrophones 722 a are disabled and the microphones 722 b are disabled.This may prevent the microphones 722 on the bottom of the housing 730(i.e., either the microphones 722 a or 722 b, depending on theorientation) from receiving muffled or otherwise distorted audio.

Further, in example implementations, the NMD may include a control totoggle between the first mode and the second mode. For instance, thehousing 730 of the NMD 703 may include a physical switch or otherhardware control to toggle between the first mode and the second mode.Alternatively, a control on a graphical user interface on a controldevice (e.g., the controller interfaces 540 of the control device 104)or voice inputs to a voice user interface may be used to toggle betweenthe first mode and the second mode. Such a control may be implemented inaddition to a toggle based on device orientation or as an alternative tothe toggle based on device orientation.

FIG. 7E is a functional block diagram showing aspects of an NMD 703configured in accordance with embodiments of the disclosure. Asdescribed in more detail below, the NMD 703 is configured to handlecertain voice inputs locally while in a first mode (and possibly also inthe second mode), without necessarily transmitting data representing thevoice input to a VAS. The NMD 703 is also configured to process othervoice inputs using a voice assistant service while the NMD 703 is in asecond mode.

Referring to the FIG. 7E, the NMD 703 includes voice capture components(“VCC”) 760, a VAS wake-word engine 770 a, and a voice extractor 773.The VAS wake-word engine 770 a and the voice extractor 773 are operablycoupled to the VCC 760. The NMD 703 a further a local voice input engine771 a operably coupled to the VCC 760.

The NMD 703 further includes microphones 722 a and 722 b (referred tocollectively as the microphones 722). The microphones 722 of the NMD 703a are configured to provide detected sound, S_(D), from the environmentof the NMD 703 to the VCC 760. The detected sound S_(D) may take theform of one or more analog or digital signals. In exampleimplementations, the detected sound S_(D) may be composed of a pluralitysignals associated with respective channels 762 a or 762 b (referred tocollectively as channels 762) that are fed to the VCC 760.

Each channel 762 may correspond to a particular microphone 722. Forexample, an NMD having six microphones may have six correspondingchannels. Each channel of the detected sound S_(D) may bear certainsimilarities to the other channels but may differ in certain regards,which may be due to the position of the given channel's correspondingmicrophone relative to the microphones of other channels. For example,one or more of the channels of the detected sound S_(D) may have agreater signal to noise ratio (“SNR”) of speech to background noise thanother channels.

As further shown in FIG. 7E, the VCC 760 includes an AEC 763, a spatialprocessor 764, and one or more buffers 768. In operation, the AEC 763receives the detected sound S_(D) and filters or otherwise processes thesound to suppress echoes and/or to otherwise improve the quality of thedetected sound S_(D). That processed sound may then be passed to thespatial processor 764.

The spatial processor 764 is typically configured to analyze thedetected sound S_(D) and identify certain characteristics, such as asound's amplitude (e.g., decibel level), frequency spectrum,directionality, etc. In one respect, the spatial processor 764 may helpfilter or suppress ambient noise in the detected sound S_(D) frompotential user speech based on similarities and differences in theconstituent channels 762 of the detected sound S_(D), as discussedabove. As one possibility, the spatial processor 764 may monitor metricsthat distinguish speech from other sounds. Such metrics can include, forexample, energy within the speech band relative to background noise andentropy within the speech band—a measure of spectral structure—which istypically lower in speech than in most common background noise. In someimplementations, the spatial processor 764 may be configured todetermine a speech presence probability, examples of such functionalityare disclosed in U.S. patent application Ser. No. 15/984,073, filed May18, 2018, titled “Linear Filtering for Noise-Suppressed SpeechDetection,” which is incorporated herein by reference in its entirety.

In operation, the one or more buffers 768—one or more of which may bepart of or separate from the memory 713 (FIG. 7A)—capture datacorresponding to the detected sound S_(D). More specifically, the one ormore buffers 768 capture detected-sound data that was processed by theupstream AEC 764 and spatial processor 766.

The network interface 724 may then provide this information to a remoteserver that may be associated with the MPS 100. In one aspect, theinformation stored in the additional buffer 769 does not reveal thecontent of any speech but instead is indicative of certain uniquefeatures of the detected sound itself. In a related aspect, theinformation may be communicated between computing devices, such as thevarious computing devices of the MPS 100, without necessarilyimplicating privacy concerns. In practice, the MPS 100 can use thisinformation to adapt and fine-tune voice processing algorithms,including sensitivity tuning as discussed below. In some implementationsthe additional buffer may comprise or include functionality similar tolookback buffers disclosed, for example, in U.S. patent application Ser.No. 15/989,715, filed May 25, 2018, titled “Determining and Adapting toChanges in Microphone Performance of Playback Devices”; U.S. patentapplication Ser. No. 16/141,875, filed Sep. 25, 2018, titled “VoiceDetection Optimization Based on Selected Voice Assistant Service”; andU.S. patent application Ser. No. 16/138,111, filed Sep. 21, 2018, titled“Voice Detection Optimization Using Sound Metadata,” which areincorporated herein by reference in their entireties.

In any event, the detected-sound data forms a digital representation(i.e., sound-data stream), S_(DS), of the sound detected by themicrophones 720. In practice, the sound-data stream S_(DS) may take avariety of forms. As one possibility, the sound-data stream S_(DS) maybe composed of frames, each of which may include one or more soundsamples. The frames may be streamed (i.e., read out) from the one ormore buffers 768 for further processing by downstream components, suchas the VAS wake-word engines 770 and the voice extractor 773 of the NMD703.

In some implementations, at least one buffer 768 captures detected-sounddata utilizing a sliding window approach in which a given amount (i.e.,a given window) of the most recently captured detected-sound data isretained in the at least one buffer 768 while older detected-sound datais overwritten when it falls outside of the window. For example, atleast one buffer 768 may temporarily retain 20 frames of a soundspecimen at given time, discard the oldest frame after an expirationtime, and then capture a new frame, which is added to the 19 priorframes of the sound specimen.

In practice, when the sound-data stream S_(DS) is composed of frames,the frames may take a variety of forms having a variety ofcharacteristics. As one possibility, the frames may take the form ofaudio frames that have a certain resolution (e.g., 16 bits ofresolution), which may be based on a sampling rate (e.g., 44,100 Hz).Additionally, or alternatively, the frames may include informationcorresponding to a given sound specimen that the frames define, such asmetadata that indicates frequency response, power input level, SNR,microphone channel identification, and/or other information of the givensound specimen, among other examples. Thus, in some embodiments, a framemay include a portion of sound (e.g., one or more samples of a givensound specimen) and metadata regarding the portion of sound. In otherembodiments, a frame may only include a portion of sound (e.g., one ormore samples of a given sound specimen) or metadata regarding a portionof sound.

In any case, downstream components of the NMD 703 may process thesound-data stream S_(DS). For instance, the VAS wake-word engines 770are configured to apply one or more identification algorithms to thesound-data stream S_(DS) (e.g., streamed sound frames) to spot potentialwake words in the detected-sound S_(D). This process may be referred toas automatic speech recognition. The VAS wake-word engine 770 a andcommand keyword engine 771 a apply different identification algorithmscorresponding to their respective wake words, and further generatedifferent events based on detecting a wake word in the detected-soundS_(D).

Example wake word detection algorithms accept audio as input and providean indication of whether a wake word is present in the audio. Manyfirst- and third-party wake word detection algorithms are known andcommercially available. For instance, operators of a voice service maymake their algorithm available for use in third-party devices.Alternatively, an algorithm may be trained to detect certain wake-words.

For instance, when the VAS wake-word engine 770 a detects a potentialVAS wake word, the VAS work-word engine 770 a provides an indication ofa “VAS wake-word event” (also referred to as a “VAS wake-word trigger”).In the illustrated example of FIG. 7A, the VAS wake-word engine 770 aoutputs a signal S_(VW) that indicates the occurrence of a VAS wake-wordevent to the voice extractor 773.

In multi-VAS implementations, the NMD 703 may include a VAS selector 774(shown in dashed lines) that is generally configured to directextraction by the voice extractor 773 and transmission of the sound-datastream S_(DS) to the appropriate VAS when a given wake-word isidentified by a particular wake-word engine (and a correspondingwake-word trigger), such as the VAS wake-word engine 770 a and at leastone additional VAS wake-word engine 770 b (shown in dashed lines). Insuch implementations, the NMD 703 may include multiple, different VASwake-word engines and/or voice extractors, each supported by arespective VAS.

Similar to the discussion above, each VAS wake-word engine 770 may beconfigured to receive as input the sound-data stream S_(DS) from the oneor more buffers 768 and apply identification algorithms to cause awake-word trigger for the appropriate VAS. Thus, as one example, the VASwake-word engine 770 a may be configured to identify the wake word“Alexa” and cause the NMD 703 a to invoke the AMAZON VAS when “Alexa” isspotted. As another example, the wake-word engine 770 b may beconfigured to identify the wake word “Ok, Google” and cause the NMD 520to invoke the GOOGLE VAS when “Ok, Google” is spotted. In single-VASimplementations, the VAS selector 774 may be omitted.

In response to the VAS wake-word event (e.g., in response to the signalS_(VW) indicating the wake-word event), the voice extractor 773 isconfigured to receive and format (e.g., packetize) the sound-data streamS_(DS). For instance, the voice extractor 773 packetizes the frames ofthe sound-data stream S_(DS) into messages. The voice extractor 773transmits or streams these messages, M_(V), that may contain voice inputin real time or near real time to a remote VAS via the network interface724.

As noted above, in the first mode, voice input processing viacloud-based voice assistant services is disabled. In some examples, todisable the voice input processing via cloud-based voice assistantservices, the NMD 703 physically or logically disables the VAS wake-wordengine(s) 770. For instance, the NMD 703 may physically or logicallyprevent the sound-data stream S_(DS) from the microphones 722 a fromreaching the VAS wake-word engine(s) 770 and/or voice extractor 773.Alternatively, suppressing generation may involve the NMD 703 ceasing tofeed the sound-data stream S_(DS) to the ASR 772. Suppressing generationmay involve gating, blocking or otherwise preventing output from the VASwake-word engine(s) 770 from generating a local keyword event.

In the second mode, voice input processing via a cloud-based voiceassistant service is enabled. The VAS is configured to process thesound-data stream S_(DS) contained in the messages M_(V) sent from theNMD 703. More specifically, in the first mode, the NMD 703 is configuredto identify a voice input 780 a captured by the microphones 722 a basedon the sound-data stream S_(DS1). In a second mode, the NMD 703 isconfigured to identify a voice input 780 a captured by the microphones772 b based on the sound-data stream S_(DS2). The voice inputs 780 a and780 b are referred to collectively as a voice input 780 and thesound-data streams S_(DS1) and S_(DS2) are referred to collectively as aS_(DS).

As described in connection with FIG. 2C, the voice input 780 may includea keyword portion and an utterance portion. The keyword portion maycorrespond to detected sound that causes a VAS wake-word event (i.e., aVAS wake word). Alternatively, the keyword portion may correspond to alocal wake word or a command keyword, which may generate a localwake-word event.

For instance, when the voice input 780 b includes a VAS wake word, thekeyword portion corresponds to detected sound that causes the wake-wordengine 770 a to output the wake-word event signal S_(VW) to the voiceextractor 773. The utterance portion in this case corresponds todetected sound that potentially comprises a user request following thekeyword portion.

When a VAS wake-word event occurs, the VAS may first process the keywordportion within the sound-data stream S_(DS) to verify the presence of aVAS wake word. In some instances, the VAS may determine that the keywordportion comprises a false wake word (e.g., the word “Election” when theword “Alexa” is the target VAS wake word). In such an occurrence, theVAS may send a response to the NMD 703 with an instruction for the NMD703 to cease extraction of sound data, which causes the voice extractor773 to cease further streaming of the detected-sound data to the VAS.The VAS wake-word engine 770 a may resume or continue monitoring soundspecimens until it spots another potential VAS wake word, leading toanother VAS wake-word event. In some implementations, the VAS does notprocess or receive the keyword portion but instead processes only theutterance portion.

In any case, the VAS processes the utterance portion to identify thepresence of any words in the detected-sound data and to determine anunderlying intent from these words. The words may correspond to one ormore commands, as well as certain keywords. The keyword may be, forexample, a word in the voice input identifying a particular device orgroup in the MPS 100. For instance, in the illustrated example, thekeyword may be one or more words identifying one or more zones in whichthe music is to be played, such as the Living Room and the Dining Room(FIG. 1A).

To determine the intent of the words, the VAS is typically incommunication with one or more databases associated with the VAS (notshown) and/or one or more databases (not shown) of the MPS 100. Suchdatabases may store various user data, analytics, catalogs, and otherinformation for natural language processing and/or other processing. Insome implementations, such databases may be updated for adaptivelearning and feedback for a neural network based on voice-inputprocessing. In some cases, the utterance portion may include additionalinformation such as detected pauses (e.g., periods of non-speech)between words spoken by a user, as shown in FIG. 2C. The pauses maydemarcate the locations of separate commands, keywords, or otherinformation spoke by the user within the utterance portion.

After processing the voice input, the VAS may send a response to the MPS100 with an instruction to perform one or more actions based on anintent it determined from the voice input. For example, based on thevoice input, the VAS may direct the MPS 100 to initiate playback on oneor more of the playback devices 102, control one or more of theseplayback devices 102 (e.g., raise/lower volume, group/ungroup devices,etc.), or turn on/off certain smart devices, among other actions. Afterreceiving the response from the VAS, the wake-word engine 770 a of theNMD 703 may resume or continue to monitor the sound-data stream S_(DS1)until it spots another potential wake-word, as discussed above.

In general, the one or more identification algorithms that a particularVAS wake-word engine, such as the VAS wake-word engine 770 a, appliesare configured to analyze certain characteristics of the detected soundstream S_(DS) and compare those characteristics to correspondingcharacteristics of the particular VAS wake-word engine's one or moreparticular VAS wake words. For example, the wake-word engine 770 a mayapply one or more identification algorithms to spot spectralcharacteristics in the detected sound stream S_(DS) that match thespectral characteristics of the engine's one or more wake words, andthereby determine that the detected sound S_(D) comprises a voice inputincluding a particular VAS wake word.

In some implementations, the one or more identification algorithms maybe third-party identification algorithms (i.e., developed by a companyother than the company that provides the NMD 703 a). For instance,operators of a voice service (e.g., AMAZON) may make their respectivealgorithms (e.g., identification algorithms corresponding to AMAZON'sALEXA) available for use in third-party devices (e.g., the NMDs 103),which are then trained to identify one or more wake words for theparticular voice assistant service. Additionally, or alternatively, theone or more identification algorithms may be first-party identificationalgorithms that are developed and trained to identify certain wake wordsthat are not necessarily particular to a given voice service. Otherpossibilities also exist.

As noted above, the NMD 703 a also includes a local voice input engine771 a in parallel with the VAS wake-word engine 770 a. Like the VASwake-word engine 770 a, the local voice input keyword engine 771 a mayapply one or more identification algorithms corresponding to one or morewake words. A “local keyword event” is generated when a particular localkeyword is identified in the detected-sound S_(D). Local keywords maytake the form of a nonce wake word corresponding to local processing(e.g., “Hey Sonos”), which is different from the VAS wake wordscorresponding to respective voice assistant services. Local keywords mayalso take the form of command keywords.

In contrast to the nonce words typically as utilized as VAS wake words,command keywords function as both the activation word and the commanditself. For instance, example command keywords may correspond toplayback commands (e.g., “play,” “pause,” “skip,” etc.) as well ascontrol commands (“turn on”), among other examples. Under appropriateconditions, based on detecting one of these command keywords, the NMD703 a performs the corresponding command.

The local voice input engine 771 a can employ an automatic speechrecognizer 772. The ASR 772 is configured to output phonetic or phenomicrepresentations, such as text corresponding to words, based on sound inthe sound-data stream S_(DS) to text. For instance, the ASR 772 maytranscribe spoken words represented in the sound-data stream S_(DS) toone or more strings representing the voice input 780 as text. The localvoice input engine 771 a can feed ASR output (labeled as S_(ASR)) to alocal natural language unit (NLU) 779 that identifies particularkeywords as being local keywords for invoking local-keyword events, asdescribed below.

As noted above, in some example implementations, the NMD 703 isconfigured to perform natural language processing, which may be carriedout using an onboard natural language processor, referred to herein as anatural language unit (NLU) 779. The local NLU 779 is configured toanalyze text output of the ASR 772 of the local voice input keywordengine 771 a to spot (i.e., detect or identify) keywords in the voiceinput 780. In FIG. 7A, this output is illustrated as the signal S_(ASR).The local NLU 779 includes a library of keywords (i.e., words andphrases) corresponding to respective commands and/or parameters.

In one aspect, the library of the local NLU 779 includes local keywords,which, as noted above, may take the form of nonce keywords or commandkeywords. When the local NLU 779 identifies a local keyword in thesignal S_(ASR), the local voice input engine 771 a generates a localkeyword event. If the identified local keyword is a command keyword, theNMD 703 performs a command corresponding to the command keyword in thesignal S_(ASR), assuming that one or more conditions corresponding tothat command keyword are satisfied. If the identified local keyword is anonce keyword, the local NLU 779 attempts to identify a keyword orkeywords corresponding to a command in the signal S_(ASR).

Further, the library of the local NLU 779 may also include keywordscorresponding to parameters. The local NLU 779 may then determine anunderlying intent from the matched keywords in the voice input 780. Forinstance, if the local NLU matches the keywords “David Bowie” and“kitchen” in combination with a play command, the local NLU 779 maydetermine an intent of playing David Bowie in the Kitchen 101 h on theplayback device 102 i. In contrast to a processing of the voice input780 by a cloud-based VAS, local processing of the voice input 780 by thelocal NLU 779 may be relatively less sophisticated, as the NLU 779 doesnot have access to the relatively greater processing capabilities andlarger voice databases that a VAS generally has access to.

In some examples, the local NLU 779 may determine an intent with one ormore slots, which correspond to respective keywords. For instance,referring back to the play David Bowie in the Kitchen example, whenprocessing the voice input, the local NLU 779 may determine that anintent is to play music (e.g., intent=playMusic), while a first slotincludes David Bowie as target content (e.g., slot1=DavidBowie) and asecond slot includes the Kitchen 101 h as the target playback device(e.g., slot2=kitchen). Here, the intent (to “playMusic”) is based on thecommand keyword and the slots are parameters modifying the intent to aparticular target content and playback device.

Within examples, the local voice input engine 771 a outputs a signal,S_(LW), that indicates the occurrence of a local keyword event to thelocal NLU 779. In response to the local keyword event (e.g., in responseto the signal S_(LW) indicating the command keyword event), the localNLU 779 is configured to receive and process the signal S_(ASR). Inparticular, the local NLU 779 looks at the words within the signalS_(ASR) to find keywords that match keywords in the library of the localNLU 779.

Some error in performing local automatic speech recognition is expected.Within examples, the ASR 772 may generate a confidence score whentranscribing spoken words to text, which indicates how closely thespoken words in the voice input 780 matches the sound patterns for thatword. In some implementations, generating a local keyword event is basedon the confidence score for a given local keyword. For instance, thelocal voice input engine 771 a may generate a command keyword event whenthe confidence score for a given sound exceeds a given threshold value(e.g., 0.5 on a scale of 0-1, indicating that the given sound is morelikely than not the local keyword). Conversely, when the confidencescore for a given sound is at or below the given threshold value, thecommand keyword engine 771 a does not generate the local keyword event.

Similarly, some error in performing keyword matching is expected. Withinexamples, the local NLU may generate a confidence score when determiningan intent, which indicates how closely the transcribed words in thesignal S_(ASR) match the corresponding keywords in the library of thelocal NLU. In some implementations, performing an operation according toa determined intent is based on the confidence score for keywordsmatched in the signal S_(ASR). For instance, the NMD 703 may perform anoperation according to a determined intent when the confidence score fora given sound exceeds a given threshold value (e.g., 0.5 on a scale of0-1, indicating that the given sound is more likely than not the commandkeyword). Conversely, when the confidence score for a given intent is ator below the given threshold value, the NMD 703 does not perform theoperation according to the determined intent.

As noted above, in some implementations, a phrase may be used as a localkeyword, which provides additional syllables to match (or not match).For instance, the phrase “Hey, Sonos” has more syllables than “Sonos,”which provides additional sound patterns to match to words. As anotherexample, the phrase “play me some music” has more syllables than “play,”which provides additional sound patterns to match to words. Accordingly,local keywords that are phrases may generally be less prone to falsewake words.

In example implementations, the NMD 703 generates a local keyword eventbased on a local keyword taking the form of a command keyword (andperforms a command corresponding to the detected command keyword) onlywhen certain conditions corresponding to a detected command keyword aremet. These conditions are intended to lower the prevalence of falsepositive command keyword events. For instance, after detecting thecommand keyword “skip,” the NMD 703 a generates a command keyword event(and skips to the next track) only when certain playback conditionsindicating that a skip should be performed are met. These playbackconditions may include, for example, (i) a first condition that a mediaitem is being played back, (ii) a second condition that a queue isactive, and (iii) a third condition that the queue includes a media itemsubsequent to the media item being played back. If any of theseconditions are not satisfied, the command keyword event is not generated(and no skip is performed).

The NMD 703 may include one or more state machine(s) 775 to facilitatedetermining whether the appropriate conditions are met. An example statemachine 775 a transitions between a first state and a second state basedon whether one or more conditions corresponding to the detected commandkeyword are met. In particular, for a given command keywordcorresponding to a particular command requiring one or more particularconditions, the state machine 775 a transitions into a first state whenone or more particular conditions are satisfied and transitions into asecond state when at least one condition of the one or more particularconditions is not satisfied.

Within example implementations, the command conditions are based onstates indicated in state variables. As noted above, the devices of theMPS 100 may store state variables describing the state of the respectivedevice. For instance, the playback devices 102 may store state variablesindicating the state of the playback devices 102, such as the audiocontent currently playing (or paused), the volume levels, networkconnection status, and the like). These state variables are updated(e.g., periodically, or based on an event (i.e., when a state in a statevariable changes)) and the state variables further can be shared amongthe devices of the MPS 100, including the NMD 703.

Similarly, the NMD 703 may maintain these state variables (either byvirtue of being implemented in a playback device or as a stand-aloneNMD). The state machine(s) 775 monitor the states indicated in thesestate variables, and determines whether the states indicated in theappropriate state variables indicate that the command condition(s) aresatisfied. Based on these determinations, the state machines 775transition between the first state and the second state, as describedabove.

In some implementations, the local voice input engine 771 is disabledunless certain conditions have been met via the state machines 775. Forexample, the first state and the second state of the state machine 775 amay operate as enable/disable toggles to the local voice input engine771 a. In particular, while a state machine 775 a corresponding to aparticular command keyword is in the first state, the state machine 775a enables the local voice input engine 771 a of the particular commandkeyword. Conversely, while the state machine 775 a corresponding to theparticular command keyword is in the second state, the state machine 775a disables the local voice input engine 771 a of the particular commandkeyword. Accordingly, the disabled local voice input engine 771 a ceasesanalyzing the sound-data stream S_(DS).

In such cases when at least one command condition is not satisfied, theNMD 703 may suppress generation of local keyword events when the localvoice input engine 771 a detects a local keyword. Suppressing generationmay involve gating, blocking or otherwise preventing output from thelocal voice input engine 771 a from generating a local keyword event.Alternatively, suppressing generation may involve the NMD 703 ceasing tofeed the sound-data stream S_(DS) to the ASR 772. Such suppressionprevents a command corresponding to the detected local keyword frombeing performed when at least one command condition is not satisfied. Insuch embodiments, the local voice input engine 771 a may continueanalyzing the sound-data stream S_(DS) while the state machine 775 a isin the first state, but command keyword events are disabled.

Other example conditions may be based on the output of a voice activitydetector (“VAD”) 765. The VAD 765 is configured to detect the presence(or lack thereof) of voice activity in the sound-data stream S_(DS). Inparticular, the VAD 765 may analyze frames corresponding to the pre-rollportion of the voice input 780 (FIG. 2D) with one or more voicedetection algorithms to determine whether voice activity was present inthe environment in certain time windows prior to a keyword portion ofthe voice input 780.

The VAD 765 may utilize any suitable voice activity detectionalgorithms. Example voice detection algorithms involve determiningwhether a given frame includes one or more features or qualities thatcorrespond to voice activity, and further determining whether thosefeatures or qualities diverge from noise to a given extent (e.g., if avalue exceeds a threshold for a given frame). Some example voicedetection algorithms involve filtering or otherwise reducing noise inthe frames prior to identifying the features or qualities.

In some examples, the VAD 765 may determine whether voice activity ispresent in the environment based on one or more metrics. For example,the VAD 765 can be configured distinguish between frames that includevoice activity and frames that don't include voice activity. The framesthat the VAD determines have voice activity may be caused by speechregardless of whether it near- or far-field. In this example and others,the VAD 765 may determine a count of frames in the pre-roll portion ofthe voice input 780 that indicate voice activity. If this count exceedsa threshold percentage or number of frames, the VAD 765 may beconfigured to output a signal or set a state variable indicating thatvoice activity is present in the environment. Other metrics may be usedas well in addition to, or as an alternative to, such a count.

The presence of voice activity in an environment may indicate that avoice input is being directed to the NMD 703. Accordingly, when the VAD765 indicates that voice activity is not present in the environment(perhaps as indicated by a state variable set by the VAD 765) this maybe configured as one of the command conditions for the local keywords.When this condition is met (i.e., the VAD 765 indicates that voiceactivity is present in the environment), the state machine 775 a willtransition to the first state to enable performing commands based onlocal keywords, so long as any other conditions for a particular localkeyword are satisfied.

Further, in some implementations, the NMD 703 may include a noiseclassifier 766. The noise classifier 766 is configured to determinesound metadata (frequency response, signal levels, etc.) and identifysignatures in the sound metadata corresponding to various noise sources.The noise classifier 766 may include a neural network or othermathematical model configured to identify different types of noise indetected sound data or metadata. One classification of noise may bespeech (e.g., far-field speech). Another classification, may be aspecific type of speech, such as background speech, and example of whichis described in greater detail with reference to FIG. 8 . Backgroundspeech may be differentiated from other types of voice-like activity,such as more general voice activity (e.g., cadence, pauses, or othercharacteristics) of voice-like activity detected by the VAD 765.

For example, analyzing the sound metadata can include comparing one ormore features of the sound metadata with known noise reference values ora sample population data with known noise. For example, any features ofthe sound metadata such as signal levels, frequency response spectra,etc. can be compared with noise reference values or values collected andaveraged over a sample population. In some examples, analyzing the soundmetadata includes projecting the frequency response spectrum onto aneigenspace corresponding to aggregated frequency response spectra from apopulation of NMDs. Further, projecting the frequency response spectrumonto an eigenspace can be performed as a pre-processing step tofacilitate downstream classification.

In various embodiments, any number of different techniques forclassification of noise using the sound metadata can be used, forexample machine learning using decision trees, or Bayesian classifiers,neural networks, or any other classification techniques. Alternativelyor additionally, various clustering techniques may be used, for exampleK-Means clustering, mean-shift clustering, expectation-maximizationclustering, or any other suitable clustering technique. Techniques toclassify noise may include one or more techniques disclosed in U.S.application Ser. No. 16/227,308 filed Dec. 20, 2018, and titled“Optimization of Network Microphone Devices Using Noise Classification,”which is herein incorporated by reference in its entirety.

In some implementations, the additional buffer 769 (shown in dashedlines) may store information (e.g., metadata or the like) regarding thedetected sound S_(D) that was processed by the upstream AEC 763 andspatial processor 764. This additional buffer 769 may be referred to asa “sound metadata buffer.” Examples of such sound metadata include: (1)frequency response data, (2) echo return loss enhancement measures, (3)voice direction measures; (4) arbitration statistics; and/or (5) speechspectral data. In example implementations, the noise classifier 766 mayanalyze the sound metadata in the buffer 769 to classify noise in thedetected sound S_(D).

As noted above, one classification of sound may be background speech,such as speech indicative of far-field speech and/or speech indicativeof a conversation not involving the NMD 703. The noise classifier 766may output a signal and/or set a state variable indicating thatbackground speech is present in the environment. The presence of voiceactivity (i.e., speech) in the pre-roll portion of the voice input 780indicates that the voice input 780 might not be directed to the NMD 703,but instead be conversational speech within the environment. Forinstance, a household member might speak something like “our kids shouldhave a play date soon” without intending to direct the command keyword“play” to the NMD 703.

Further, when the noise classifier indicates that background speech ispresent is present in the environment, this condition may disable thevoice input engine 771 a. In some implementations, the condition ofbackground speech being absent in the environment (perhaps as indicatedby a state variable set by the noise classifier 766) is configured asone of the command conditions for the command keywords. Accordingly, thestate machine 775 a will not transition to the first state when thenoise classifier 766 indicates that background speech is present in theenvironment.

Further, the noise classifier 766 may determine whether backgroundspeech is present in the environment based on one or more metrics. Forexample, the noise classifier 766 may determine a count of frames in thepre-roll portion of the voice input 780 that indicate background speech.If this count exceeds a threshold percentage or number of frames, thenoise classifier 766 may be configured to output the signal or set thestate variable indicating that background speech is present in theenvironment. Other metrics may be used as well in addition to, or as analternative to, such a count.

Within example implementations, the NMD 703 a may support a plurality oflocal keywords. To facilitate such support, the local voice input engine771 a may implement multiple identification algorithms corresponding torespective local keywords. Alternatively, the NMD 703 a may implementadditional local voice input engines 771 b configured to identifyrespective local keywords. Yet further, the library of the local NLU 779may include a plurality of local keywords and be configured to searchfor text patterns corresponding to these command keywords in the signalS_(ASR).

Further, local keywords may require different conditions. For instance,the conditions for “skip” may be different than the conditions for“play” as “skip” may require that the condition that a media item isbeing played back and play may require the opposite condition that amedia item is not being played back. To facilitate these respectiveconditions, the NMD 703 a may implement respective state machines 775corresponding to each local keyword. Alternatively, the NMD 703 mayimplement a state machine 775 having respective states for each commandkeyword. Other examples are possible as well.

Further techniques related to conditioning of local keyword events andVAS wake word events are described in in U.S. application Ser. No.16/439,009 filed Jun. 12, 2019, and titled “Network Microphone DeviceWith Command Keyword Conditioning,” which is herein incorporated byreference in its entirety.

Referring still to FIG. 7E, in example embodiments, the VAS wake-wordengine 770 a and the local voice input engine 771 a may take a varietyof forms. For example, the VAS wake-word engine 770 a and the localvoice input engine 771 a may take the form of one or more modules thatare stored in memory of the NMD 703 (e.g., the memory 713 of FIG. 7A).As another example, the VAS wake-word engine 770 a and the local voiceinput engine 771 a may take the form of a general-purposes orspecial-purpose processor, or modules thereof. In this respect, multipleengines 770 and 771 may be part of the same component of the NMD 703 oreach engine 770 and 771 may take the form of a component that isdedicated for the particular wake-word engine. Other possibilities alsoexist.

In some implementations, in the second mode, voice input processing viaa cloud-based VAS and local voice input processing are concurrentlyenabled. A user may speak a local keyword to invoke local processing ofa voice input 780 b via the local voice input engine 771 a. Notably,even in the second mode, the NMD 703 may forego sending any datarepresenting the detected sound S_(D) (e.g., the messages M_(V)) to aVAS when processing a voice input 780 b including a local keyword.Rather, the voice input 780 b is processed locally using the local voiceinput engine 771 a. Accordingly, speaking a voice input 780 b (with alocal keyword) to the NMD 703 may provide increased privacy relative toother NMDs that process all voice inputs using a VAS.

As indicated above, some keywords in the library of the local NLU 779correspond to parameters. These parameters may define to perform thecommand corresponding to a detected command keyword. When keywords arerecognized in the voice input 780, the command corresponding to thedetected command keyword is performed according to parameterscorresponding to the detected keywords.

For instance, an example voice input 780 may be “play music at lowvolume” with “play” being the command keyword portion (corresponding toa playback command) and “music at low volume” being the voice utteranceportion. When analyzing this voice input 780, the NLU 779 may recognizethat “low volume” is a keyword in its library corresponding to aparameter representing a certain (low) volume level. Accordingly, theNLU 779 may determine an intent to play at this lower volume level.Then, when performing the playback command corresponding to “play,” thiscommand is performed according to the parameter representing a certainvolume level.

In a second example, another example voice input 780 may be “play myfavorites in the Kitchen” with “play” again being the command keywordportion (corresponding to a playback command) and “my favorites in theKitchen” as the voice utterance portion. When analyzing this voice input780, the NLU 779 may recognize that “favorites” and “Kitchen” matchkeywords in its library. In particular, “favorites” corresponds to afirst parameter representing particular audio content (i.e., aparticular playlist that includes a user's favorite audio tracks) while“Kitchen” corresponds to a second parameter representing a target forthe playback command (i.e., the kitchen 101 h zone. Accordingly, the NLU779 may determine an intent to play this particular playlist in thekitchen 101 h zone.

In a third example, a further example voice input 780 may be “volume up”with “volume” being the command keyword portion (corresponding to avolume adjustment command) and “up” being the voice utterance portion.When analyzing this voice input 780, the NLU 779 may recognize that “up”is a keyword in its library corresponding to a parameter representing acertain volume increase (e.g., a 10 point increase on a 100 point volumescale). Accordingly, the NLU 779 may determine an intent to increasevolume. Then, when performing the volume adjustment commandcorresponding to “volume,” this command is performed according to theparameter representing the certain volume increase.

Other example voice inputs may relate to smart device commands. Forinstance, an example voice input 780 may be “turn on patio lights” with“turn on” being the command keyword portion (corresponding to a power oncommand) and “patio lights” being the voice utterance portion. Whenanalyzing this voice input 780, the NLU 779 may recognize that “patio”is a keyword in its library corresponding to a first parameterrepresenting a target for the smart device command (i.e., the patio 101i zone) and “lights” is a keyword in its library corresponding to asecond parameter representing certain class of smart device (i.e., smartillumination devices, or “smart lights”) in the patio 101 i zone.Accordingly, the NLU 779 may determine an intent to turn on smart lightsassociated with the patio 101 i zone. As another example, anotherexample voice input 780 may be “set temperature to 75” with “settemperature” being the command keyword portion (corresponding to athermostat adjustment command) and “to 75” being the voice utteranceportion. When analyzing this voice input 780, the NLU 779 may recognizethat “to 75” is a keyword in its library corresponding to a parameterrepresenting a setting for the thermostat adjustment command.Accordingly, the NLU 779 may determine an intent to set a smartthermostat to 75 degrees.

Within examples, certain command keywords are functionally linked to asubset of the keywords within the library of the local NLU 779, whichmay hasten analysis. For instance, the command keyword “skip” may befunctionality linked to the keywords “forward” and “backward” and theircognates. Accordingly, when the command keyword “skip” is detected in agiven voice input 780, analyzing the voice utterance portion of thatvoice input 780 with the local NLU 779 may involve determining whetherthe voice input 780 includes any keywords that match these functionallylinked keywords (rather than determining whether the voice input 780includes any keywords that match any keyword in the library of the localNLU 779). Since vastly fewer keywords are checked, this analysis isrelatively quicker than a full search of the library. By contrast, anonce VAS wake word such as “Alexa” provides no indication as to thescope of the accompanying voice input.

Some commands may require one or more parameters, as such the commandkeyword alone does not provide enough information to perform thecorresponding command. For example, the command keyword “volume” mightrequire a parameter to specify a volume increase or decrease, as theintent of “volume” of volume alone is unclear. As another example, thecommand keyword “group” may require two or more parameters identifyingthe target devices to group.

Accordingly, in some example implementations, when a given commandkeyword is detected in the voice input 780 by the command keyword engine771 a, the local NLU 779 may determine whether the voice input 780includes keywords matching keywords in the library corresponding to therequired parameters. If the voice input 780 does include keywordsmatching the required parameters, the NMD 703 a proceeds to perform thecommand (corresponding to the given command keyword) according to theparameters specified by the keywords.

However, if the voice input 780 does include keywords matching therequired parameters for the command, the NMD 703 a may prompt the userto provide the parameters. For instance, in a first example, the NMD 703a may play an audible prompt such as “I've heard a command, but I needmore information” or “Can I help you with something?” Alternatively, theNMD 703 a may send a prompt to a user's personal device via a controlapplication (e.g., the software components 132 c of the controldevice(s) 104).

In further examples, the NMD 703 a may play an audible prompt customizedto the detected command keyword. For instance, after detect a commandkeyword corresponding to a volume adjustment command (e.g., “volume”),the audible prompt may include a more specific request such as “Do youwant to adjust the volume up or down?” As another example, for agrouping command corresponding to the command keyword “group,” theaudible prompt may be “Which devices do you want to group?” Supportingsuch specific audible prompts may be made practicable by supporting arelatively limited number of command keywords (e.g., less than 100), butother implementations may support more command keywords with thetrade-off of requiring additional memory and processing capability.

Within additional examples, when a voice utterance portion does notinclude keywords corresponding to one or more required parameters, theNMD 703 a may perform the corresponding command according to one or moredefault parameters. For instance, if a playback command does not includekeywords indicating target playback devices 102 for playback, the NMD703 a may default to playback on the NMD 703 a itself (e.g., if the NMD703 a is implemented within a playback device 102) or to playback on oneor more associated playback devices 102 (e.g., playback devices 102 inthe same room or zone as the NMD 703 a). Further, in some examples, theuser may configure default parameters using a graphical user interface(e.g., user interface 430) or voice user interface. For example, if agrouping command does not specify the playback devices 102 to group, theNMD 703 a may default to instructing two or more pre-configured defaultplayback devices 102 to form a synchrony group. Default parameters maybe stored in data storage and accessed when the NMD 703 a determinesthat keywords exclude certain parameters. Other examples are possible aswell.

In some implementations, while in the second mode, the NMD 703 a sendsthe voice input 780 to a VAS when the local NLU 779 is unable to processthe voice input 780 (e.g., when the local NLU is unable to find matchesto keywords in the library, or when the local NLU 779 has a lowconfidence score as to intent). In an example, to trigger sending thevoice input 780, the NMD 703 a may generate a bridging event, whichcauses the voice extractor 773 to process the sound-data stream S_(D),as discussed above. That is, the NMD 703 a generates a bridging event totrigger the voice extractor 773 without a VAS wake-word being detectedby the VAS wake-word engine 770 a (instead based on a command keyword inthe voice input 780, as well as the NLU 779 being unable to process thevoice input 780).

Before sending the voice input 780 to the VAS (e.g., via the messagesM_(V)), the NMD 703 a may obtain confirmation from the user that theuser acquiesces to the voice input 780 being sent to the VAS. Forinstance, the NMD 703 a may play an audible prompt to send the voiceinput to a default or otherwise configured VAS, such as “I'm sorry, Ididn't understand that. May I ask Alexa?” In another example, the NMD703 a may play an audible prompt using a VAS voice (i.e., a voice thatis known to most users as being associated with a particular VAS), suchas “Can I help you with something?” In such examples, generation of thebridging event (and trigging of the voice extractor 773) is contingenton a second affirmative voice input 780 from the user.

Within certain example implementations, while in the first mode, thelocal NLU 779 may process the signal S_(ASR) without necessarily a localkeyword event being generated by the command keyword engine 771 a (i.e.,directly). That is, the automatic speech recognition 772 may beconfigured to perform automatic speech recognition on the sound-datastream S_(D), which the local NLU 779 processes for matching keywordswithout requiring a local keyword event. If keywords in the voice input780 are found to match keywords corresponding to a command (possiblywith one or more keywords corresponding to one or more parameters), theNMD 703 a performs the command according to the one or more parameters.

Further, in such examples, the local NLU 779 may process the signalS_(ASR) directly only when certain conditions are met. In particular, insome embodiments, the local NLU 779 processes the signal S_(ASR) onlywhen the state machine 775 a is in the first state. The certainconditions may include a condition corresponding to no background speechin the environment. An indication of whether background speech ispresent in the environment may come from the noise classifier 766. Asnoted above, the noise classifier 766 may be configured to output asignal or set a state variable indicating that far-field speech ispresent in the environment. Further, another condition may correspond tovoice activity in the environment. The VAD 765 may be configured tooutput a signal or set a state variable indicating that voice activityis present in the environment. The prevalence of false positivedetection of commands with a direct processing approach may be mitigatedusing the conditions determined by the state machine 775 a.

In example implementations, the NMD 703 is paired with one or more smartdevices. FIG. 8A illustrates an example pairing arrangement between theNMD 703 and a smart device 802, which includes an integrated playbackdevice and smart illumination device. By pairing the NMD 703 with thesmart device(s), voice commands to control the smart device(s) may bedirected to the NMD 703 to control the smart device(s) withoutnecessarily including a keyword identifying the smart device(s) in thevoice command. For instance, commands such as “play back Better OblivionCommunity Center” and “turn on lights” are received by the NMD 703, butcarried out on the smart device 802 without necessarily identifying thesmart device 802 by name, room, zone, or the like. On the other hand, auser may still direct inputs to other smart devices in the MPS 100 byreferencing the name, room, zone, group, area, etc. that the smartdevice is associated with.

Within examples, a user may configure the pairing arrangement using agraphical user interface or voice user interface. For instance, the usermay use a GUI on a application of a control device 104 to configure thepairing arrangement. Alternatively, the user may speak a voice commandsuch as “Please pair with the Ikea® lamp” or “Please pair with theSonos® Play:1” to configure the pairing relationship. The NMD 703 maystore data representing the pairing arrangement in one or more statevariables, which may be referenced when identifying a device to carryout a voice command.

In the illustrative example of FIG. 8A, the NMD 703 is operating in thesecond mode. That is, the NMD 703 is in the second orientation and hasenabled the second mode. Voice processing via the cloud-based voiceassistant service(s) is enabled. The NMD 703 has established a localnetwork connection via the LAN 111 to the smart device 802, as well asan Internet-based connection to the VAS 190 via the network 107 (FIG.1B). Likewise, the smart device 802 has established a local networkconnection via the LAN 111 to the NMD 703, as well as an Internet-basedconnection to the VAS 190 via the network 107 (FIG. 1B).

Further, in the exemplary pairing relationship of FIG. 8A, the smartdevice 802 may play back audio responses to voice inputs. As notedabove, the NMD 703 may, in some examples, exclude audio playbackcomponents typically present in a playback device (e.g., audioprocessing components 216, amplifiers 217, and/or speakers 218) or mayinclude relatively less capable versions of these components. By pairingthe NMD 703 to a playback device, the playback device may provideplayback functions to complement the NMD, including playback of audioresponses to voice inputs captured by the NMD 703 and playback of audiocontent initiated via voice command to the NMD 703.

For instance, while in the second mode, the user may speak the voiceinput “Alexa, what is the weather,” which is captured by the microphones722 b (FIG. 7C) of the NMD 703. The NMD 703 transmits data representingthis voice input to the servers 106 a of the VAS 190. The servers 106 aprocess this voice input and provide data representing a spokenresponse. In some implementations, the smart device 802 receives thisdata directly from the computing devices 106 a of the VAS 190 via thenetworks 107 and the LAN 111. Alternatively, the NMD 703 may receive thedata from the VAS 190, but send the data to the smart device 802. Ineither case, the playback device 802 plays back the spoken response.

As noted above, in the second mode, voice input processing via the VAS190 and voice input processing via the local voice input engine 771 amay be concurrently enabled. In an example, a user may speak the voiceinput “Alexa, play ‘Hey Jude’ by the Beatles and turn on the Ikealamps.” Here, “Alexa” is an example of a VAS wake word and “Ikea” is anexample of a local keyword. Accordingly, such an input may generate botha VAS wake work event and a local keyword event on the NMD 703.

FIG. 8B again shows the exemplary pairing relationship between the NMD703 and the smart device 802. In this FIG. 8B example, the NMD 703 isoperating in the first mode, so voice input processing via the VAS 190is disabled. This state is represented by the broken lines between theLAN 111 to the networks 107. While in the first mode, the NMD 703 mayreceive voice inputs including commands to control the smart device 802.The NMD 703 may process such voice inputs via the local voice inputengine 771 a and transmit instructions to carry out the commands to thesmart device 802 via the LAN 111.

In some examples, the library of the local NLU 779 is partiallycustomized to the individual user(s). In a first aspect, the library maybe customized to the devices that are within the household of the NMD(e.g., the household within the environment 101 (FIG. 1A)). Forinstance, the library of the local NLU may include keywordscorresponding to the names of the devices within the household, such asthe zone names of the playback devices 102 in the MPS 100. In a secondaspect, the library may be customized to the users of the devices withinthe household. For example, the library of the local NLU 779 may includekeywords corresponding to names or other identifiers of a user'spreferred playlists, artists, albums, and the like. Then, the user mayrefer to these names or identifiers when directing voice inputs to thecommand keyword engine 771 a and the local NLU 779.

Within example implementations, the NMD 703 a may populate the libraryof the local NLU 779 locally within the network 111 (FIG. 1B). As notedabove, the NMD 703 a may maintain or have access to state variablesindicating the respective states of devices connected to the network 111(e.g., the playback devices 104). These state variables may includenames of the various devices. For instance, the kitchen 101 h mayinclude the playback device 101 b, which are assigned the zone name“Kitchen.” The NMD 703 a may read these names from the state variablesand include them in the library of the local NLU 779 by training thelocal NLU 779 to recognize them as keywords. The keyword entry for agiven name may then be associated with the corresponding device in anassociated parameter (e.g., by an identifier of the device, such as aMAC address or IP address). The NMD 703 a can then use the parameters tocustomize control commands and direct the commands to a particulardevice.

In further examples, the NMD 703 a may populate the library bydiscovering devices connected to the network 111. For instance, the NMD703 a may transmit discovery requests via the network 111 according to aprotocol configured for device discovery, such as universalplug-and-play (UPnP) or zero-configuration networking. Devices on thenetwork 111 may then respond to the discovery requests and exchange datarepresenting the device names, identifiers, addresses and the like tofacilitate communication and control via the network 111. The NMD 703may read these names from the exchanged messages and include them in thelibrary of the local NLU 779 by training the local NLU 779 to recognizethem as keywords.

In further examples, the NMD 703 a may populate the library using thecloud. To illustrate, FIG. 9 is a schematic diagram of the MPS 100 and acloud network 902. The cloud network 902 includes cloud servers 906,identified separately as media playback system control servers 906 a,streaming audio service servers 906 b, and IOT cloud servers 906 c. Thestreaming audio service servers 906 b may represent cloud servers ofdifferent streaming audio services. Similarly, the IOT cloud servers 906c may represent cloud servers corresponding to different cloud servicessupporting smart devices 990 in the MPS 100. Smart devices 990 includesmart illumination devices, smart thermostats, smart plugs, securitycameras, doorbells, and the like.

Within examples, a user may link an account of the MPS 100 to an accountof a IOT service. For instance, an IOT manufacturer (such as IKEA®) mayoperate a cloud-based IOT service to facilitate cloud-based control oftheir IOT products using smartphone app, website portal, and the like.In connection with such linking, keywords associated with thecloud-based service and the IOT devices may be populated in the libraryof the local NLU 779. For instance, the library may be populated with anonce keyword (e.g., “Hey Ikea”). Further, the library may be populatedwith names of various IOT devices, keyword commands for controlling theIOT devices, and keywords corresponding to parameters for the commands.

One or more communication links 903 a, 903 b, and 903 c (referred tohereinafter as “the links 903”) communicatively couple the MPS 100 andthe cloud servers 906. The links 903 can include one or more wirednetworks and one or more wireless networks (e.g., the Internet).Further, similar to the network 111 (FIG. 1B), a network 911communicatively couples the links 903 and at least a portion of thedevices (e.g., one or more of the playback devices 102, NMDs 103 and 703a, control devices 104, and/or smart devices 990) of the MPS 100.

In some implementations, the media playback system control servers 906 afacilitate populating the library of local NLU 779 with the NMD(s) 703 a(representing one or more of the NMD 703 a (FIG. 7A) within the MPS100). In an example, the media playback system control servers 906 a mayreceive data representing a request to populate the library of a localNLU 779 from the NMD 703. Based on this request, the media playbacksystem control servers 906 a may communicate with the streaming audioservice servers 906 b and/or IOT cloud servers 906 c to obtain keywordsspecific to the user.

In some examples, the media playback system control servers 906 a mayutilize user accounts and/or user profiles in obtaining keywordsspecific to the user. As noted above, a user of the MPS 100 may set-up auser profile to define settings and other information within the MPS100. The user profile may then in turn be registered with user accountsof one or more streaming audio services to facilitate streaming audiofrom such services to the playback devices 102 of the MPS 100.

Through use of these registered streaming audio services, the streamingaudio service servers 906 b may collect data indicating a user's savedor preferred playlists, artists, albums, tracks, and the like, eithervia usage history or via user input (e.g., via a user input designatinga media item as saved or a favorite). This data may be stored in adatabase on the streaming audio service servers 906 b to facilitateproviding certain features of the streaming audio service to the user,such as custom playlists, recommendations, and similar features. Underappropriate conditions (e.g., after receiving user permission), thestreaming audio service servers 906 b may share this data with the mediaplayback system control servers 906 a over the links 903 b.

Accordingly, within examples, the media playback system control servers906 a may maintain or have access to data indicating a user's saved orpreferred playlists, artists, albums, tracks, genres, and the like. If auser has registered their user profile with multiple streaming audioservices, the saved data may include saved playlists, artists, albums,tracks, and the like from two or more streaming audio services. Further,the media playback system control servers 906 a may develop a morecomplete understanding of the user's preferred playlists, artists,albums, tracks, and the like by aggregating data from the two or morestreaming audio services, as compared with a streaming audio servicethat only has access to data generated through use of its own service.

Moreover, in some implementations, in addition to the data shared fromthe streaming audio service servers 906 b, the media playback systemcontrol servers 906 a may collect usage data from the MPS 100 over thelinks 903 a, after receiving user permission. This may include dataindicating a user's saved or preferred media items on a zone basis.Different types of music may be preferred in different rooms. Forinstance, a user may prefer upbeat music in the Kitchen 101 h and moremellow music to assist with focus in the Office 101 e.

Using the data indicating a user's saved or preferred playlists,artists, albums, tracks, and the like, the media playback system controlservers 906 a may identify names of playlists, artists, albums, tracks,and the like that the user is likely to refer to when providing playbackcommands to the NMDs 703 via voice input. Data representing these namescan then be transmitted via the links 903 a and the network 904 to theNMDs 703 a and then added to the library of the local NLU 779 askeywords. For instance, the media playback system control servers 906 amay send instructions to the NMD 703 to include certain names askeywords in the library of the local NLU 779. Alternatively, the NMD 703(or another device of the MPS 100) may identify names of playlists,artists, albums, tracks, and the like that the user is likely to referto when providing playback commands to the NMD 703 via voice input andthen include these names in the library of the local NLU 779.

Due to such customization, similar voice inputs may result in differentoperations being performed when the voice input is processed by thelocal NLU 779 as compared with processing by a VAS. For instance, afirst voice input of “Alexa, play me my favorites in the Office” maytrigger a VAS wake-word event, as it includes a VAS wake word (“Alexa”).A second voice input of “Play me my favorites in the Office” may triggera command keyword, as it includes a command keyword (“play”).Accordingly, the first voice input is sent by the NMD 703 a to the VAS,while the second voice input is processed by the local NLU 779.

While these voice inputs are nearly identical, they may cause differentoperations. In particular, the VAS may, to the best of its ability,determine a first playlist of audio tracks to add to a queue of theplayback device 102 f in the office 101 e. Similarly, the local NLU 779may recognize keywords “favorites” and “kitchen” in the second voiceinput. Accordingly, the NMD 703 a performs the voice command of “play”with parameters of <favorites playlist> and <kitchen 101 h zone>, whichcauses a second playlist of audio tracks to be added to the queue of theplayback device 102 f in the office 101 e. However, the second playlistof audio tracks may include a more complete and/or more accuratecollection of the user's favorite audio tracks, as the second playlistof audio tracks may draw on data indicating a user's saved or preferredplaylists, artists, albums, and tracks from multiple streaming audioservices, and/or the usage data collected by the media playback systemcontrol servers 906 a. In contrast, the VAS may draw on its relativelylimited conception of the user's saved or preferred playlists, artists,albums, and tracks when determining the first playlist.

To illustrate, FIG. 11 shows a table 1100 illustrating the respectivecontents of a first and second playlist determined based on similarvoice inputs, but processed differently. In particular, the firstplaylist is determined by a VAS while the second playlist is determinedby the NMD 703 a (perhaps in conjunction with the media playback systemcontrol servers 906 a). As shown, while both playlists purport toinclude a user's favorites, the two playlists include audio content fromdissimilar artists and genres. In particular, the second playlist isconfigured according to usage of the playback device 102 f in the Office101 e and also the user's interactions with multiple streaming audioservices, while the first playlist is based on the multiple user'sinteractions with the VAS. As a result, the second playlist is moreattuned to the types of music that the user prefers to listen to in theoffice 101 e (e.g., indie rock and folk) while the first playlist ismore representative of the interactions with the VAS as a whole.

A household may include multiple users. Two or more users may configuretheir own respective user profiles with the MPS 100. Each user profilemay have its own user accounts of one or more streaming audio servicesassociated with the respective user profile. Further, the media playbacksystem control servers 906 a may maintain or have access to dataindicating each user's saved or preferred playlists, artists, albums,tracks, genres, and the like, which may be associated with the userprofile of that user.

In various examples, names corresponding to user profiles may bepopulated in the library of the local NLU 779. This may facilitatereferring to a particular user's saved or preferred playlists, artists,albums, tracks, or genres. For instance, when a voice input of “PlayAnne's favorites on the patio” is processed by the local NLU 779, thelocal NLU 779 may determine that “Anne” matches a stored keywordcorresponding to a particular user. Then, when performing the playbackcommand corresponding to that voice input, the NMD 703 a adds a playlistof that particular user's favorite audio tracks to the queue of theplayback device 102 c in the patio 101 i.

In some cases, a voice input might not include a keyword correspondingto a particular user, but multiple user profiles are configured with theMPS 100. In some cases, the NMD 703 a may determine the user profile touse in performing a command using voice recognition. Alternatively, theNMD 703 a may default to a certain user profile. Further, the NMD 703 amay use preferences from the multiple user profiles when performing acommand corresponding to a voice input that did not identify aparticular user profile. For instance, the NMD 703 a may determine afavorites playlist including preferred or saved audio tracks from eachuser profile registered with the MPS 100.

The IOT cloud servers 906 c may be configured to provide supportingcloud services to the smart devices 990. The smart devices 990 mayinclude various “smart” internet-connected devices, such as lights,thermostats, cameras, security systems, appliances, and the like. Forinstance, an IOT cloud server 906 c may provide a cloud servicesupporting a smart thermostat, which allows a user to control the smartthermostat over the internet via a smartphone app or website.

Accordingly, within examples, the IOT cloud servers 906 c may maintainor have access to data associated with a user's smart devices 990, suchas device names, settings, and configuration. Under appropriateconditions (e.g., after receiving user permission), the IOT cloudservers 906 c may share this data with the media playback system controlservers 906 a and/or the NMD 703 a via the links 903 c. For instance,the IOT cloud servers 906 c that provide the smart thermostat cloudservice may provide data representing such keywords to the NMD 703 a,which facilitates populating the library of the local NLU 779 withkeywords corresponding to the temperature.

Yet further, in some cases, the IOT cloud servers 906 c may also providekeywords specific to control of their corresponding smart devices 990.For instance, the IOT cloud server 906 c that provides the cloud servicesupporting the smart thermostat may provide a set of keywordscorresponding to voice control of a thermostat, such as “temperature,”“warmer,” or “cooler,” among other examples. Data representing suchkeywords may be sent to the NMDs 703 a over the links 903 and thenetwork 904 from the IOT cloud servers 906 c.

As noted above, some households may include more than NMD 703. Inexample implementations, two or more NMDs 703 may synchronize orotherwise update the libraries of their respective local NLU 779. Forinstance, a first NMD 703 a and a second NMD 703 b may share datarepresenting the libraries of their respective local NLU 779, possiblyusing a network (e.g., the network 904). Such sharing may facilitate theNMDs 703 a being able to respond to voice input similarly, among otherpossible benefits.

In some embodiments, one or more of the components described above canoperate in conjunction with the microphones 720 to detect and store auser's voice profile, which may be associated with a user account of theMPS 100. In some embodiments, voice profiles may be stored as and/orcompared to variables stored in a set of command information or datatable. The voice profile may include aspects of the tone or frequency ofa user's voice and/or other unique aspects of the user, such as thosedescribed in previously-referenced U.S. patent application Ser. No.15/438,749.

In some embodiments, one or more of the components described above canoperate in conjunction with the microphones 720 to determine thelocation of a user in the home environment and/or relative to a locationof one or more of the NMDs 103. Techniques for determining the locationor proximity of a user may include one or more techniques disclosed inpreviously-referenced U.S. patent application Ser. No. 15/438,749, U.S.Pat. No. 9,084,058 filed Dec. 29, 2011, and titled “Sound FieldCalibration Using Listener Localization,” and U.S. Pat. No. 8,965,033filed Aug. 31, 2012, and titled “Acoustic Optimization.” Each of theseapplications is herein incorporated by reference in its entirety.

FIGS. 10A, 10B, 10C, and 10D show exemplary input and output from theNMD 703 configured in accordance with aspects of the disclosure.

FIG. 10A illustrates a first scenario in which a wake-word engine of theNMD 703 is configured to detect four local keywords (“play”, “stop”,“resume”, “turn on”). The local NLU 779 (FIG. 7E) is disabled. In thisscenario, the user has spoken the voice input “turn on” to the NMD 703,which triggers a new recognition of one of the local keywords (e.g., acommand keyword event corresponding to turn on).

Yet further, the VAD 765 and noise classifier 766 (FIG. 7E) haveanalyzed 150 frames of a pre-roll portion of the voice input. As shown,the VAD 765 has detected voice in 140 frames of the 150 pre-roll frames,which indicates that a voice input may be present in the detected sound.Further, the noise classifier 766 has detected ambient noise in 11frames, background speech in 127 frames, and fan noise in 12 frames. Inthis example, the noise classifier 766 is classifying the predominantnoise source in each frame. This indicates the presence of backgroundspeech. As a result, the NMD has determined not to trigger on thedetected local keyword “turn on.”

FIG. 10B illustrates a second scenario in which the local voice inputengine 771 a of the NMD 703 is configured to detect a local keyword(“play”) as well as two cognates of that command keyword (“playsomething” and “play me a song”). The local NLU 779 is disabled. In thissecond scenario, the user has spoken the voice input “play something” tothe NMD 703, which triggers a new recognition of one of the localkeywords (e.g., a command keyword event).

Yet further, the VAD 765 and noise classifier 766 have analyzed 150frames of a pre-roll portion of the voice input. As shown, the VAD 765has detected voice in 87 frames of the 150 pre-roll frames, whichindicates that a voice input may be present in the detected sound.Further, the noise classifier 766 has detected ambient noise in 18frames, background speech in 8 frames, and fan noise in 124 frames. Thisindicates that background speech is not present. Given the foregoing,the NMD 703 has determined to trigger on the detected local keyword“play.”

FIG. 10C illustrates a third scenario in which the local voice inputengine 771 a of the NMD 703 is configured to detect three local keywords(“play”, “stop”, and “resume”). The local NLU 779 is enabled. In thisthird scenario, the user has spoken the voice input “play Beatles in theKitchen” to the NMD 703, which triggers a new recognition of one of thelocal keywords (e.g., a command keyword event corresponding to play).

As shown, the ASR 772 has transcribed the voice input as “play beet lesin the kitchen.” Some error in performing ASR is expected (e.g., “beetles”). Here, the local NLU 779 has matched the keyword “beet les” to“The Beatles” in the local NLU library, which sets up this artist as acontent parameter to the play command. Further, the local NLU 779 hasalso matched the keyword “kitchen” to “kitchen” in the local NLUlibrary, which sets up the kitchen zone as a target parameter to theplay command. The local NLU produced a confidence score of0.63428231948273443 associated with the intent determination.

Here as well, the VAD 765 and noise classifier 766 have analyzed 150frames of a pre-roll portion of the voice input. As shown, the noiseclassifier 766 has detected ambient noise in 142 frames, backgroundspeech in 8 frames, and fan noise in 0 frames. This indicates thatbackground speech is not present. The VAD 765 has detected voice in 112frames of the 150 pre-roll frames, which indicates that a voice inputmay be present in the detected sound. Here, the NMD 703 has determinedto trigger on the detected command keyword “play.”

FIG. 10D illustrates a fourth scenario in which the local voice inputengine 771 a of the NMD is not configured to spot any local keywords.Rather, the local voice input engine 771 a will perform ASR and pass theoutput of the ASR to the local NLU 779. The local NLU 779 is enabled andconfigured to detect keywords corresponding to both commands andparameters. In the fourth scenario, the user has spoken the voice input“play some music in the Office” to the NMD 703.

As shown, the ASR 772 has transcribed the voice input as “lay some musicin the office.” Here, the local NLU 779 has matched the keyword “lay” to“play” in the local NLU library, which corresponds to a playbackcommand. Further, the local NLU 779 has also matched the keyword“office” to “office” in the local NLU library, which sets up the office101 e zone as a target parameter to the play command. The local NLU 779produced a confidence score of 0.14620494842529297 associated with thekeyword matching. In some examples, this low confidence score may causethe NMD to not accept the voice input (e.g., if this confidence score isbelow a threshold, such as 0.5).

IV. Example VAS Toggle Techniques

FIG. 11 is a flow diagram showing an example method 1100 to toggle voiceinput processing based on device orientation. The method 1100 may beperformed by a networked microphone device, such as the NMD 703 (FIG.7A). Alternatively, the method 1100 may be performed by any suitabledevice or by a system of devices, such as the playback devices 103, NMDs103, control devices 104, computing devices 105, computing devices 106,and/or NMD 703.

At block 1102, the method 1100 involves detecting that the housing is ina first orientation. For instance, one or more orientation sensors(e.g., the orientation sensor(s) 723 (FIG. 7A)) may generate dataindicative of the orientation of the NMD 703. The NMD 703 may detectthat the housing 730 is in a first orientation (FIG. 7B).

In some implementations, the NMD 703 is configured to generate eventswhen the orientation of the NMD 703 changes. Such events may triggermode changes in the NMD 703. For instance, when the housing 730 isswitched from a second orientation (FIG. 7C) to a second orientation(FIG. 7B), the orientation sensors 723 may generate data indicative ofacceleration of the NMD 703. The NMD 703 may determine that this dataindicates that the housing 730 is in the first orientation and generatean event indicating this orientation. In some examples, the orientationstate of the NMD 703 is stored in one or more state variables, which canbe referenced to determine the current orientation of the NMD 703.

At block 1104, the method 1100 involves enabling a first mode. Enablingthe first mode involves disabling voice input processing via acloud-based voice assistant service and enabling voice input processingvia a local natural language unit, such as the NLU 779 (FIG. 7E).Enabling the first mode may further involve enabling one or more firstmicrophones (e.g., the microphones 722 a (FIG. 7B)) and/or disabling oneor more second microphones (e.g., the microphones 722 b (FIG. 7C)).

In some examples, the NMD 703 enables the first mode after detectingthat the housing is in a first orientation. As noted above, detectingthat the housing is in a first orientation may involve detecting anevent. For example, the NMD 703 may enable the first mode based on aparticular event being generated where the particular event correspondsto a change in orientation from the second orientation to the firstorientation.

The first mode may remain enabled while the housing is in the firstorientation. In some examples, while in the first mode, the NMD 703 maydirectly (e.g., via orientation sensor(s) 723) or indirectly (e.g., viathe one or more state variables) determine whether the NMD 703 is stillin the first orientation. If the NMD 703 determines that the NMD 703 isno longer in the first orientation, the NMD 703 may switch modes.

At block 1106, the method 1100 involves receiving a voice input.Receiving a voice input may involve capturing sound data associated witha first voice input 780 a via the one or more first microphones 722 a(FIG. 7E). Receiving the voice input may further involve detecting, viaa local natural language unit 779, that the first voice input comprisessound data matching one or more keywords from a local natural languageunit library of the local natural language unit 779. For instance, localnatural language unit 779 may determine that the voice input includesone or more local keywords that generate a local keyword event, such asa nonce local keyword and/or a command keyword, as well as one or moreadditional keywords that correspond to parameters of the voice command.

At block 1108, the method 1100 involves determining, via the localnatural language unit, an intent of the first voice input based on atleast one of the one or more keywords. For instance, the NLU 779 maydetermine that the voice input includes a particular command keyword(e.g., turn on) and one or more keywords corresponding to parameters(e.g., the lights) and determine an intent of turning on the lights on apaired smart device 802 (FIG. 8B).

At block 1110, the method 1100 involves performing a first commandaccording to the determined intent of the first voice input. Performingthe first command may involve sending instructions to one or morenetwork devices over a network to perform one or more operationsaccording to the first command, similar to the message exchangeillustrated in FIG. 6 . For instance, the NMD 703 may transmit aninstruction over the LAN 111 to the smart device 802 to toggle thelights or to play back audio content.

Within examples, the target network devices to perform the first commandmay be explicitly or implicitly defined. For example, the target smartdevices may be explicitly defined by reference in the voice input 780 tothe name(s) of one or more smart devices (e.g., by reference to a room,zone or zone group name). Alternatively, the voice input might notinclude any reference to the name(s) of one or more smart devices andinstead may implicitly refer to smart device(s) paired with the NMD 703.Playback devices 102 associated with the NMD 703 a may include aplayback device implementing the NMD 703 a, as illustrated by theplayback device 102 d implementing the NMD 103 d (FIG. 1B)) or playbackdevices configured to be associated (e.g., where the playback devices102 are in the same room or area as the NMD 703 a).

Further, performing the first command may involve sending instructionsto one or more remote computing devices. For example, the NMD 703 maytransmit requests to the computing devices 106 of the MCS 192 to streamone or more audio tracks to the smart device 902 (FIG. 8B).Alternatively, the instructions may be provided internally (e.g., over alocal bus or other interconnection system) to one or more software orhardware components (e.g., the electronics 112 of the playback device102).

Yet further, transmitting instructions may involve both local and cloudbased operations. For instance, the NMD 703 may transmit instructionslocally over the LAN 111 to the smart device 802 to add one or moreaudio tracks to the playback queue over the LAN 111. Then, the smartdevice 802 may transmit a request to the computing devices 106 of theMCS 192 to stream one or more audio tracks to the smart device 802 forplayback over the networks 107. Other examples are possible as well.

At block 1112, the method 1100 involves detecting that the housing is ina second orientation different than the first orientation. For instance,one or more orientation sensors (e.g., the orientation sensor(s) 723(FIG. 7A)) may generate data indicative of the orientation of the NMD703. The NMD 703 may detect that the housing 730 is in a firstorientation (FIG. 7B).

As noted above, in some implementations, the NMD 703 is configured togenerate events when the orientation of the NMD 703 changes. Such eventsmay trigger mode changes in the NMD 703. For instance, when the housing730 is switched from the first orientation to the second orientation(FIG. 7D), the orientation sensors 723 may generate data indicative ofacceleration of the NMD 703. The NMD 703 may determine that this dataindicates that the housing 730 is in the second orientation and generatean event indicating this orientation.

At block 1114, the method 1100 involves enabling a second mode. Enablingthe second mode involves enabling voice input processing via acloud-based voice assistant service. In some implementations, enablingthe second mode also includes disabling voice input processing via alocal natural language unit, such as the NLU 779 (FIG. 7E).Alternatively, voice input processing via the local natural languageunit may remain enabled in the second mode. Enabling the second mode mayfurther involve enabling one or more second microphones (e.g., themicrophones 722 b (FIG. 7C)) and/or disabling one or more firstmicrophones (e.g., the microphones 722 a (FIG. 7B)).

In some examples, the NMD 703 enables the second mode after detectingthat the housing is in a second orientation. As noted above, detectingthat the housing is in a first orientation may involve detecting anevent. For example, the NMD 703 may enable the second mode based on aparticular event being generated where the particular event correspondsto a change in orientation from the second orientation to the firstorientation. Other examples are possible as well.

CONCLUSION

The description above discloses, among other things, various examplesystems, methods, apparatus, and articles of manufacture including,among other components, firmware and/or software executed on hardware.It is understood that such examples are merely illustrative and shouldnot be considered as limiting. For example, it is contemplated that anyor all of the firmware, hardware, and/or software aspects or componentscan be embodied exclusively in hardware, exclusively in software,exclusively in firmware, or in any combination of hardware, software,and/or firmware. Accordingly, the examples provided are not the onlyway(s) to implement such systems, methods, apparatus, and/or articles ofmanufacture.

The specification is presented largely in terms of illustrativeenvironments, systems, procedures, steps, logic blocks, processing, andother symbolic representations that directly or indirectly resemble theoperations of data processing devices coupled to networks. These processdescriptions and representations are typically used by those skilled inthe art to most effectively convey the substance of their work to othersskilled in the art. Numerous specific details are set forth to provide athorough understanding of the present disclosure. However, it isunderstood to those skilled in the art that certain embodiments of thepresent disclosure can be practiced without certain, specific details.In other instances, well known methods, procedures, components, andcircuitry have not been described in detail to avoid unnecessarilyobscuring aspects of the embodiments. Accordingly, the scope of thepresent disclosure is defined by the appended claims rather than theforgoing description of embodiments.

When any of the appended claims are read to cover a purely softwareand/or firmware implementation, at least one of the elements in at leastone example is hereby expressly defined to include a tangible,non-transitory medium such as a memory, DVD, CD, Blu-ray, and so on,storing the software and/or firmware.

The present technology is illustrated, for example, according to variousaspects described below. Various examples of aspects of the presenttechnology are described as numbered examples (1, 2, 3, etc.) forconvenience. These are provided as examples and do not limit the presenttechnology. It is noted that any of the dependent examples may becombined in any combination, and placed into a respective independentexample. The other examples can be presented in a similar manner.

Example 1: A method to be performed by a network microphone deviceincluding one or more first microphones, one or more second microphones,a network interface, one or more processors, and a housing carrying theone or more first microphones, the one or more second microphones, thenetwork interface, the one or more processors, and data storage havingstored therein instructions executable by the one or more processors.The network microphone device detects that the housing is in a firstorientation. After detecting that the housing is in the firstorientation, the device enables a first mode. Enabling the first modeincludes (i) disabling voice input processing via a cloud-based voiceassistant service and (ii) enabling voice input processing via a localnatural language unit. While the first mode is enabled, the networkmicrophone device (i) captures sound data associated with a first voiceinput via the one or more first microphones and (ii) detects, via alocal natural language unit, that the first voice input comprises sounddata matching one or more keywords from a local natural language unitlibrary of the local natural language unit. The network microphonedevice determines, via the local natural language unit, an intent of thefirst voice input based on at least one of the one or more keywords andperforms a first command according to the determined intent of the firstvoice input. The network microphone device may detects that the housingis in a second orientation different than the first orientation. Afterdetecting that the housing is in the second orientation, the networkmicrophone device enables the second mode. Enabling the second modeincludes enabling voice input processing via the cloud-based voiceassistant service.

Example 2: The method of Example 1, wherein enabling the first modefurther comprises disabling the one or more second microphones.

Example 3: The method of any of Examples 1 and 2, wherein enabling thesecond mode further comprises at least one of: (a) disabling the one ormore first microphones or (b) disabling voice input processing via thelocal natural language unit.

Example 4: The method of any of Examples 1-3, further comprising pairingthe NMD to a network device and wherein performing the first commandcomprises transmitting an instruction over a local area network to thenetwork device.

Example 5: The method of any of Examples 4, wherein the network devicecomprises a smart illumination device, and wherein the first command isa command to toggle the smart illumination device on or off.

Example 6: The method of any of Example 4, wherein the functions furthercomprise pairing the NMD to a playback device separate from the networkdevice, wherein the playback device is configured to process playbackcommands transmitted to the playback device from one or more remotecomputing devices of the cloud-based voice-assistant service.

Example 7: The method of any of Examples 1-6, further comprising whilethe second mode is enabled, (i) detecting a sound data stream associatedwith a second voice input; (ii) detecting a wake-word in the secondsound data stream; and (iii) after detecting the wake-word, transmittingthe second sound data stream to one or more remote computing devices ofthe cloud-based voice-assistant service.

Example 8: The method of any of Examples 1-8, wherein the networkmicrophone device further comprises one or more sensors carried in thehousing wherein detecting that the housing is in a second orientationdifferent than the first orientation comprises detecting, via the one orsensors, sensor data indicating that the housing has been re-orientedfrom the first orientation to the second orientation.

Example 9: A tangible, non-transitory, computer-readable medium havinginstructions stored thereon that are executable by one or moreprocessors to cause a playback device to perform the method of any oneof Examples 1-8.

Example 10: A playback device comprising a speaker, a network interface,one or more microphones configured to detect sound, one or moreprocessors, and a tangible, non-tangible computer-readable medium havinginstructions stored thereon that are executable by the one or moreprocessors to cause the playback device to perform the method of any ofExamples 1-8.

The invention claimed is:
 1. A network microphone device comprising: atleast one physical control; one or more microphones; a networkinterface; at least one processor; and a housing carrying the one ormore microphones, the network interface, the at least one processor, anddata storage including instructions that are executable by the at leastone processor such that the network microphone device is configured to:detect a first input to the at least one physical control; afterdetection of the first input to the at least one physical control,enable a first mode, wherein the instructions that are executable by theat least one processor such that the network microphone device isconfigured to enable the first mode comprise instructions that areexecutable by the at least one processor such that the networkmicrophone device is configured to: (a) disable voice input processingvia a cloud-based voice assistant service and (b) enable voice inputprocessing via a local natural language unit; while the first mode isenabled: (i) capture sound data associated with a first voice input viathe one or more microphones and (ii) detect, via the local naturallanguage unit, that the first voice input comprises sound data matchingone or more keywords from a local natural language unit library of thelocal natural language unit; determine, via the local natural languageunit, an intent of the first voice input based on at least one of theone or more keywords; perform a first command according to thedetermined intent of the first voice input; detect a second input to theat least one physical control; and after detection of the second inputto the at least one physical control, enable a second mode, wherein theinstructions that are executable by the at least one processor such thatthe network microphone device is configured to enable the second modecomprise instructions that are executable by the at least one processorsuch that the network microphone device is configured to: (a) enablevoice input processing via the cloud-based voice assistant service and(b) disable voice input processing via the local natural language unit.2. The network microphone device of claim 1, wherein the at least onephysical control comprises a physical toggle control, and wherein theinstructions that are executable by the at least one processor such thatthe network microphone device is configured to detect the second inputto the at least one physical control comprise instructions that areexecutable by the at least one processor such that the networkmicrophone device is configured to: detect that the second input toggledthe physical toggle control from a first position associated with thefirst mode to a second position associated with the second mode.
 3. Thenetwork microphone device of claim 1, wherein the network microphonedevice is paired to a network internet-of-things (IoT) device, andwherein the instructions that are executable by the at least oneprocessor such that the network microphone device is configured toperform the first command comprise instructions that are executable bythe at least one processor such that the network microphone device isconfigured to: transmit, via the network interface over a local areanetwork, an instruction to the network IoT device.
 4. The networkmicrophone device of claim 3, wherein the network IoT device comprises asmart illumination device, and wherein the first command comprises acommand to toggle an illumination state of the smart illuminationdevice.
 5. The network microphone device of claim 3, wherein the networkmicrophone device is paired to a playback device carried in a separatehousing from the network IoT device, and wherein the playback device isconfigured to process playback commands transmitted to the playbackdevice from one or more remote computing devices of the cloud-basedvoice assistant service.
 6. The network microphone device of claim 1,wherein the instructions are executable by the at least one processorsuch that the network microphone device is further configured to: whilethe second mode is enabled, (i) capture additional sound data associatedwith a second voice input via the one or more microphones; (ii) detect awake-word in the additional sound data; and (iii) after detection of thewake-word, transmit at least a portion of the additional sound data toone or more remote computing devices of the cloud-based voice assistantservice for processing of the second voice input.
 7. The networkmicrophone device of claim 1, wherein the one or more microphonescomprise at least one first microphone and at least one secondmicrophone, wherein the instructions that are executable by the at leastone processor such that the network microphone device is configured toenable the first mode comprise instructions that are executable by theat least one processor such that the network microphone device isconfigured to: (a) enable the at least one first microphone and (b)disable the at least one second microphone, and wherein the instructionsthat are executable by the at least one processor such that the networkmicrophone device is configured to enable the second mode compriseinstructions that are executable by the at least one processor such thatthe network microphone device is configured to: (a) disable the at leastone first microphone and (b) enable the at least one second microphone.8. A system comprising: a network microphone device, the networkmicrophone device comprising: at least one physical control; one or moremicrophones; a network interface; at least one processor; and a housingcarrying the one or more microphones, the network interface, the atleast one processor, and data storage including instructions that areexecutable by the at least one processor such that the networkmicrophone device is configured to: detect a first input to the at leastone physical control; after detection of the first input to the at leastone physical control, enable a first mode, wherein the instructions thatare executable by the at least one processor such that the networkmicrophone device is configured to enable the first mode compriseinstructions that are executable by the at least one processor such thatthe network microphone device is configured to: (a) disable voice inputprocessing via a cloud-based voice assistant service and (b) enablevoice input processing via a local natural language unit; while thefirst mode is enabled: (i) capture sound data associated with a firstvoice input via the one or more microphones and (ii) detect, via thelocal natural language unit, that the first voice input comprises sounddata matching one or more keywords from a local natural language unitlibrary of the local natural language unit; determine, via the localnatural language unit, an intent of the first voice input based on atleast one of the one or more keywords; perform a first command accordingto the determined intent of the first voice input; detect a second inputto the at least one physical control; and after detection of the secondinput to the at least one physical control, enable a second mode,wherein the instructions that are executable by the at least oneprocessor such that the network microphone device is configured toenable the second mode comprise instructions that are executable by theat least one processor such that the network microphone device isconfigured to: (a) enable voice input processing via the cloud-basedvoice assistant service and (b) disable voice input processing via thelocal natural language unit.
 9. The system of claim 8, wherein the atleast one physical control comprises a physical toggle control, andwherein the instructions that are executable by the at least oneprocessor such that the network microphone device is configured todetect the second input to the at least one physical control compriseinstructions that are executable by the at least one processor such thatthe network microphone device is configured to: detect that the secondinput toggled the physical toggle control from a first positionassociated with the first mode to a second position associated with thesecond mode.
 10. The system of claim 8, further comprising a playbackdevice that is paired to the network microphone device, and wherein theinstructions that are executable by the at least one processor such thatthe network microphone device is configured to perform the first commandcomprise instructions that are executable by the at least one processorsuch that the network microphone device is configured to: transmit, viathe network interface over a local area network, an instruction to theplayback device to play back audio content via one or more audiotransducers of the playback device.
 11. The system of claim 8, whereinthe network microphone device is paired to a network internet-of-things(IoT) device, and wherein the instructions that are executable by the atleast one processor such that the network microphone device isconfigured to perform the first command comprise instructions that areexecutable by the at least one processor such that the networkmicrophone device is configured to: transmit, via the network interfaceover a local area network, an instruction to the network IoT device. 12.The system of claim 11, wherein the network IoT device comprises a smartillumination device, and wherein the first command comprises a commandto toggle an illumination state of the smart illumination device. 13.The system of claim 8, wherein the instructions are executable by the atleast one processor such that the network microphone device is furtherconfigured to: while the second mode is enabled, (i) capture additionalsound data associated with a second voice input via the one or moremicrophones; (ii) detect a wake-word in the additional sound data; and(iii) after detection of the wake-word, transmit at least a portion ofthe additional sound data to one or more remote computing devices of thecloud-based voice assistant service for processing of the second voiceinput.
 14. The system of claim 8, wherein the one or more microphonescomprise at least one first microphone and at least one secondmicrophone, wherein the instructions that are executable by the at leastone processor such that the network microphone device is configured toenable the first mode comprise instructions that are executable by theat least one processor such that the network microphone device isconfigured to: (a) enable the at least one first microphone and (b)disable the at least one second microphone, and wherein the instructionsthat are executable by the at least one processor such that the networkmicrophone device is configured to enable the second mode compriseinstructions that are executable by the at least one processor such thatthe network microphone device is configured to: (a) disable the at leastone first microphone and (b) enable the at least one second microphone.15. A method to be performed by a network microphone device comprisingat least one physical control and one or more microphones, the methodcomprising: detecting a first input to the at least one physicalcontrol; after detecting the first input to the at least one physicalcontrol, enabling a first mode, wherein enabling the first modecomprises: (a) disabling voice input processing via a cloud-based voiceassistant service and (b) enabling voice input processing via a localnatural language unit; while the first mode is enabled: (i) capturingsound data associated with a first voice input via the one or moremicrophones and (ii) detecting, via the local natural language unit,that the first voice input comprises sound data matching one or morekeywords from a local natural language unit library of the local naturallanguage unit; determining, via the local natural language unit, anintent of the first voice input based on at least one of the one or morekeywords; performing a first command according to the determined intentof the first voice input; detecting a second input to the at least onephysical control; and after detecting the second input to the at leastone physical control, enabling a second mode, wherein enabling thesecond mode comprises: (a) enabling voice input processing via thecloud-based voice assistant service and (b) disabling voice inputprocessing via the local natural language unit.
 16. The method of claim15, wherein the at least one physical control comprises a physicaltoggle control, and wherein detecting the second input to the at leastone physical control comprises: detecting that the second input toggledthe physical toggle control from a first position associated with thefirst mode to a second position associated with the second mode.
 17. Themethod of claim 15, wherein the network microphone device is paired to anetwork internet-of-things (IoT) device, and wherein performing thefirst command comprises: transmitting, via a network interface of thenetwork microphone device over a local area network, an instruction tothe network IoT device.
 18. The method of claim 17, wherein the networkIoT device comprises a smart illumination device, and wherein the firstcommand comprises a command to toggle an illumination state of the smartillumination device.
 19. The method of claim 17, wherein the networkmicrophone device is paired to a playback device carried in a separatehousing from the network IoT device, and wherein the playback device isconfigured to process playback commands transmitted to the playbackdevice from one or more remote computing devices of the cloud-basedvoice assistant service.
 20. The method of claim 15, further comprising:while the second mode is enabled, (i) capturing additional sound dataassociated with a second voice input via the one or more microphones;(ii) detecting a wake-word in the additional sound data; and (iii) afterdetecting the wake-word, transmitting at least a portion of theadditional sound data to one or more remote computing devices of thecloud-based voice assistant service for processing of the second voiceinput.